Naomi Lees, NetIKX Membership Secretary writes:

Thank you to everyone who responded to our NetIKX survey earlier this year. We had some very interesting and useful responses.

Here is a brief overview of the points raised, and what NetIKX plans to do over the next 12 months:

Programme Planning

We had some very useful feedback on the seminar topics you would like to see, especially around the future and value of KIM; as well as practical KIM tools and techniques. You will be pleased to know that we will be covering all these aspects and more in our programme in 2017 and early 2018, so check www.netikx.org/events or https://netikx.wordpress.com/events/ for details of future events.

We also had some other useful suggestions for future seminar topics, which our programme planner has taken away for further cogitation! Watch this space for further updates.

Events outside London

You said that you would like to see more events outside London – we are currently looking at ways we can make this happen. If you are keen to host an event outside London, please get in touch.

Partnering with other KIM Groups

We had some very encouraging feedback on developing partnerships with other KIM groups. You will be pleased to know that we have a KIM Communities event coming up soon. We are always interested in building connections with other KIM groups, so please get in touch if you have any ideas for joint-working.

NetIKX Website

We received several comments on the website and we are really grateful for this feedback. You will be pleased to know that we are currently working on a new website, with lots of the features you have asked for, such as more KIM resources and the ability to make electronic payments.

The survey results can be viewed here: https://www.surveymonkey.com/results/SM-VZXFF523/

 

Conrad Taylor writes:

On Thursday 3rd of November 2016, about thirty people gathered at the British Dental Association to discuss the topic of ‘Evidence-Based Decision Making’, in a workshop-like session led by Steve Dale, who practises as an independent consultant as ‘Collabor8Now’.

The NetIKX difference

Before I give readers an account of the meeting, and some thinking about it, I’ll describe a few things that often make NetIKX meetings ‘different’ from those in other organisations devoted to information and knowledge management. This meeting was a good expression of those differences.

For one thing, NetIKX is not dominated by academics – most who come to the meetings work with knowledge and information in government departments, business corporations, agencies and the like. That majority is then seasoned with a sprinkling of consultants who work in those kinds of business environment.

Secondly, the pattern of most NetIKX meetings is to have one or two thought-provoking presentations, followed by discussions or exercises in ‘table groups’ (called syndicate sessions). This on average occupies a third of the time, followed by pooling of ideas in a brief plenary. That’s quite different from the pattern of lecture plus brief Q&A encountered at so many other organisations’ meetings.

When you combine those two features – the nature of the audience and the participatory table-group engagement – the Network for Information and Knowledge Exchange does live up to its ‘network’ title pretty well. The way Steve organised this meeting, with a heavier than usual slant towards table-group activity, made the most of this opportunity for encounter and exchange.

Setting the scene

Steve explained that he had already delivered this ‘package’ in other contexts, including for the Knowledge and Innovation Network (KIN) associated with Warwick Business School (http://www.ki-network.org/jm/index.php). We know Steve is also interested in the idea of ‘gamifying’ processes: he hoped the work he had prepared for us would be fun. There would even be an element of competition between the five tables, with a prize at stake.

Steve started with a proposition: ‘Decisions should always be based on a combination of critical thinking and the best available evidence’. Also, he offered us a dictionary definition of Evidence, namely, ‘the available body of facts or information indicating whether a belief or proposition is true or valid’.

The first proposition, of course, begs the question about what you consider to be the best available evidence – whose opinions you trust, for example. That, it turned out, was the question at the heart of Steve’s second exercise for us.

As for that ‘definition’, I have my doubts. It could be interpreted as saying that we start with a ‘belief or proposition’, and then stack information around it to support that point of view. That may be how politics and tabloid journalism works, but I am more comfortable with scientific investigation.

There are at least two ways in which science looks at evidence. If an explanatory hypothesis is being tested, the experiment is framed in such a way that evidence from it may overthrow the hypothesis, forcing us to modify it. And very often, before there is yet a basis for confidently putting forth a hypothesis, ‘evidence’ in the form of observed facts or measurements, and even apparent correlations, is worth taking note of anyway: this then constitutes something that requires explaining. Two cases in point would be field notebooks in biology and series measurements in meteorology.

Similarly, ‘evidence’ in a forensic investigation should float free of argument, and may support any number of causal constructions (unless you are trying to fit somebody up). That’s what makes detective fiction fun to read.

Certainly, in our complex world, we do need the best possible evidence, but it is often far from easy to determine just what that is, let alone how to interpret it. I shall end this piece with a few personal thoughts about that.

Correlation and causation

Steve’s following slides explored what happens when you confuse ‘correlation’ (things, especially trends, which happen within the same context of time and space) with ‘causation’. For example: just as Internet Explorer was losing market share, there was a parallel decline in the murder rate; from the start of the Industrial Revolution, average global temperatures have been trending upwards, closely correlated with a decline in piracy on the high seas. Did the former cause the latter, or the other way round?

Those, of course, are deliberately silly examples. But often, correlation may usefully give us a hint about where to look for a causal mechanism. The global warming trend has been found (after much data collection at an observatory in Hawaii) to correlate with an increase in the proportion of carbon dioxide in the atmosphere. That observation spurred research into the ‘greenhouse gas’ effect, helping us to understand the dynamics of climate change. As for the field of medicine, where causative proof is hard to nail down, sometimes correlation alone is deemed convincing enough to guide action: thus NICE recommends donepezil as a palliative treatment for Alzheimer’s, though its precise mechanism of action is unproven.

Data visualisation

Steve then moved the focus on to one particular way in which information claiming to be ‘evidence’ is shoved at us these days – data visualisation, which we may define as the use of graphical images (charts, graphs, data maps) to present data to an audience. He mentioned a project called Seeing Data, a collaboration between British and Norwegian universities, which is exploring the role of data visualisations in society (see http://seeingdata.org). According to this project, the key skills we need to work with data visualisations are…

• language skills;
• mathematical and statistical skills, including a familiarity with chart types and how to interpret them;
• computer skills, for those cases where the visualisation is an interactive one;
• and skills in critical thinking, such as those that may lead us to question the assumptions, or detect a ‘spin’ being put on the facts.

Steve showed a few visualisations that may require an effort to understand, including the London Underground ‘Tube map’ (more properly, a network diagram). Some people, said Steve, have problems using this to get from one place to another. Actually, a geographically accurate map of the Underground looks like a dense tangle of spaghetti at the centre with dangling strands at the periphery. Harry Beck’s famous diagram, much imitated by other transport networks, is simplified and distorted to focus attention on the ‘lines’ and the stations, especially those that serve as interconnectors. But it is certainly not intended as a guide to direction or distance: using it to plan a walking tour would be a big mistake.

One might therefore say that effective understanding of a diagram requires experience of that diagram type and its conventions: a sub-type of the factor (2) in the list above. Charts, graphs, diagrams and data maps are highly formalised semiotic expressions. Partly because of that formalism, but also because many visualisations are designed to support fast expert analysis, we would be wrong to expect every visualisation to be understood by just anyone. Even the experienced technicians who recently did my echocardiogram defer to the consultant cardiologist, when it comes to interpreting the visualised data.

Critical thinking in focus

For our first exercise, Steve wanted us to apply critical thinking to ten given situations, set out in a document, copies of which were shared with each table group. Five of these puzzlers were illustrated with a graphic. To prime us, he talked through a number of images. In one case, a chart indicating changing quantities over time, the vertical axis representing the quantities did not start at zero (a common phenomenon): it gave the impression of a large change over time, which wasn’t warranted by the data. A map of referendum voting patterns across the counties and regions of Scotland could skew one’s impressions owing to the vast area of the sparsely populated Highlands, Galloway etc., compared to the small but densely settled zones of Glasgow and Lanarkshire. Other examples illustrated problems of sampling biases.

The exercises were quite fun. One of my favourites, and it did bamboozle me, showed a side elevation and plan picture of a twin-engined Douglas Dakota cargo plane marked with loads of red dots. The accompanying text said that the RAF had responded to losses of their planes to German anti-aircraft fire by examining the ones which got back, to see where the damage had occurred. They had aggregated the data (that is what the red dots indicated) and analysed the diagram to determine where to apply protective armour. What we were supposed to notice was that clearly, as those planes had managed to return, being struck in those marked places was usually survivable. The fact that no such dots showed up on the cockpit or either engine was because strikes in those locations tended to be fatal.

I won’t go through all of the examples in the exercise. In one case we were supposed to analyse trends in death by firearms year after year, but the y axis had been inverted, turning the curve upside down. In another case, the y axis was organised by a geometric progression rather than a linear one (each extra increment represented a doubling of quantity). That was quite a weird example, but bear in mind that logarithmic scales are common in some scientific graphs – and are used appropriately there, and understood by their intended audience.

It was fun working with the team on my table. We were pretty good at identifying, in some cases, multiple points of criticism. That rather undermined our score, because Steve decreed there should be only one criticism per example, and his answers had to be regarded as the right ones for the purpose of the competition! But the real benefit was the process, analysis and discussion.

Whose evidence do you trust?

The second exercise painted the scenario of an Italian company developing software for the retail sector: the concern was to know whether introducing performance-related pay would improve productivity in the engineering teams.

Steve had concocted eight forms of ‘evidence’ from various sources: a senior external consultant who said ‘no, you need to develop the leadership skills of supervisors’; a trusted friend who pointed to a study from the London School of Economics; an article in the Financial Times; a Harvard study of productivity amongst Chinese mineworkers; various responses to the question posted on a Human Resources discussion forum; what the HR director thinks. There were also two bits of evidence closer to the company: data about discrepancies in performance between the existing teams, which seemed to indicate that the most productive teams were those with a high proportion of senior engineers; and information that the union representing most of the engineers had resisted previous attempts at performance-related pay differentials.

We were supposed to rank these inputs on the basis of how trustworthy we thought their sources to be; my table found it quite hard to avoid considering how relevant the offered evidence might be. For example, we didn’t think the circumstances of Italian software engineers and Chinese mineworkers to be remotely comparable. I found it interesting how many of us tended to regard people like consultants and top management as trustworthy, whereas, when employees’ union was mentioned, people said, ‘Oh, they’ll be biased’. There is obviously a lot of subjectivity involved in evaluating sources.

If one thinks more broadly of evaluating the relevance and validity of evidence on offer, it appears to have at least two components: the degree to which the experience or model offered has both internal coherence and similarity to the situation for which a decision is being sought; and evaluation of the ‘messenger’ bringing those ideas. Thus there is a danger that useful evidence might be disregarded because of bias against the source.

Personal reflections

This was certainly a lively and highly engaged meeting, and Steve must be congratulated for how he structured the ‘table work’. The tasks we were set may have been artificial, and I thought some of the conclusions reached could be challenged, but it made for a lot of discussion, which indeed continued when we broke into unstructured networking afterwards, with drinks and snacks.

Clearly, it is valuable to learn to be critical of data visualisations, especially now they have become so fashionable. Data visualisations are often poor because their creators have not thought properly about what is to be communicated, and to what kind of audience, or haven’t considered how these highly abstracted and formal representations may be misunderstood. (And then, of course, there’s the possibility that the purpose is deliberately to mislead!)

There is a whole different (and more political) tack that we could have explored. This was the last NetIKX meeting of 2016, a year in which we have witnessed some quite outrageous distortions of the truth around the so-called ‘Brexit’ referendum, to name but one field of discourse. More generally, the media have been guilty of over-simplified representations of many very complex issues.

This was also the year in which Michael Gove exclaimed that we’d had enough of the opinions of experts – the kind of attitude that doesn’t bode well for the prospect of ‘evidence-based government’.

In respect of Evidence-Based Decision Making, I think that to rise to urgent environmental, social, developmental and political challenges, we definitely need the best evidence and predictive modelling that we can muster. And whatever respect we as citizens have for our own intelligence, it is hubris to think that we can make sense of many of these hyper-complex situations on our own without the help of experts. But can we trust them?

The nature of that expert knowledge, how we engage with the experts and they with us, and how we apply critical thinking in respect of expert opinion – these are worthy topics for any knowledge and information management network, and not something that can be dealt with in an afternoon.

At the meeting, Dion Lindsay spoke up to propose that NetIKX might usefully find a platform or method for ongoing and extended discussions between meetings (an email list such as the KIDMM community uses is one such option, but there may be better Web-based ones). The NetIKX committee is happy to look into this – so I guess we should start looking for evidence on which to base a decision!

Conrad Taylor writes:

The September 2016 meeting of NetIKX was introduced by David Penfold. He explained that at this time in 2015, the NetIKX meeting about ‘connecting communities’ had heard from various organisations in the knowledge and information management space. This year, the decision to focus the meeting on training and development had been partly influenced by a plea at an ISKO UK meeting for more thinking about these topics.

All our speakers had interpreted the meeting topic as being about Continuous Professional Development (CPD). There were two presentations, followed in the usual NetIKX pattern by discussion in table groups. The first presentation was given by Luke Stevens-Burt, who is Head of Business Development (Member Services) at the Chartered Institute of Library and Information Professionals.

CILIP’s Professional Knowledge and Skills Base

Luke explained that CILIP accredits degree programmes at universities, and registers and certifies members though chartership and fellowship, but that their main support for the development of its members is delivered through CPD, which he defined as ‘intentionally developing the knowledge, skills and personal knowledge needed to perform professional responsibilities’.  In the past, CPD had been conceptualised as a formal training process, but there has been a shift towards understanding informal experiences and exposure to ideas as being as important, if not more so.

As a person’s work experience develops, and the world of work changes, CPD helps to bolster adaptability. The resources available for learning are diversifying, including MOOCs, journals, seminars and conferences and meetings, even informal conversations over coffee.

Central to CILIP’s support for CPD is something they call the ‘PKSB’, which stands for Professional Knowledge and Skills Base. The conceptual diagram for this – rather difficult to read because of unfortunate low-contrast colour choices – Luke called the ‘wagon wheel’. At the hub, the diagram places ‘Ethics and Values’. Radiating from this are eight spokes representing aspects of Professional Expertise, and four more spokes represent Generic Skills. Around these the diagram portrays a ‘rim’ representing the wider context of the library, information and knowledge sector, and finally an outer ‘tyre’ of an even wider context, to do with the employing organisation and its environment.

The eight headings for ‘Professional Expertise’ are: organising knowledge and information; knowledge and information management; exploiting knowledge and information; research skills; information governance and compliance; records management and archiving; collection management and development; and so-called ‘literacies’ and learning.

The more generic skillsets, which can be found in many professions, were identified as use of computers and communication, leadership and advocacy, strategic planning, and a sundry collection around customer service design and marketing.

A system for self-assessment

Fundamentally, it seems, the PKSB toolkit is a system, using which a person can make a self-assessment of their level of understanding or skill in each of these areas (and subsidiary sets under these, totalling about a hundred in all), based on a ranking between zero for ‘no understanding’ and four for the highest level of expertise.

In using the PKSB, CILIP members are supposed to define what level they are at in each skill area that’s relevant to their current job, and what level they would ideally like to attain, and add an explanatory comment. For example, a person might decide that they only score a basic ‘1’ at using classification schemes and taxonomies, but would like to make progress towards level ‘2’. An alternative use of PKSB could be for career planning, related not to your current job, but to one into which you would like to progress, which might require an upgrading of skills.

Apparently, this PKSB system is used by CILIP in deciding whether to register someone as a member, for example as a chartered member. In this case, the self-assessment is only one step, because one must also submit a portfolio, explaining how you have gained your skills, and this will go before an Assessor.

Thereafter, the PKSB is purely a self-assessment tool so that members can monitor their progress and design a CPD path for themselves. It is for use by CILIP members only, though it is also used as the framework for deciding whether university courses meet the standard at which they can be accredited by CILIP.

Until mid 2016, CILIP members used the PKSB by printing out a set of forms and maintaining them manually. The recent developments do not fundamentally change the system, but make it available as an online interactive system with an app-like interface, usable from a computer, tablet or smartphone. Much of the rest of Luke’s presentation consisted of a live demonstration of the online PKSB interface and facilities, for example showing how it can generate summary reports.

Finally, Luke touched briefly on the resources CILIP can directly provide to support professional development. Within CILIP there are a number of member networks. Some are regionally based, and some are special interest groups – such as the School Libraries Group, the Multimedia Information & Technology group, and the UK eInformation Group (UKeIG). CILIP also plans to launch a new SIG in January 2017 for knowledge and information management, as a revamp of the existing Information Services Group (it will be interesting to see whether this new body will be prepared to collaborate with others in the field, such as NetIKX, ISKO, etc).

CILIP also maintains a Virtual Learning Environment, with online modular courses, about which it would have been nice to hear more; and publishes a members’ magazine (Update), e-bulletins, and various journals, some of which are in print form and some online.

CPD in Government

The second presentation was given by Christopher Reeves and Karen Thwaites, who both work for the Department for Education – Christopher on the records management side, and Karen as a knowledge and information manager with a training role. Additionally, Christopher is on the working group for the Government Knowledge and Information Management (GKIM) Skills Framework, which was the topic of their talk.

The Knowledge and Information Management (KIM) profession has been recognised by the UK government only since the turn of the century, though there have been many jobs in the civil service with aspects of KIM within them, such as librarians and managers of information rights, and records managers. Karen displayed an ‘onion diagram’ showing a core set of KIM roles, surrounded by allied roles such as specialists in geospatial information or data scientists, and an outer rim of allied professions.

The Civil Service Reform Plan, published in June 2012, stated that civil servants should operate as ‘digital by default’, with a set of skills transferrable between the public and private sector. People with KIM roles have become more prominent lately; in the field of records management, the Hillsborough Enquiry played a role in raising a more general level of awareness, as has the current independent enquiry into child sexual abuse.

The GKIM Framework working group

A working group for the GKIM Skills Framework was announced in 2015 by Stephen Mathern as Head of Profession, and gathered under the chairmanship of David Elder to review an existing Framework and propose revisions. The volunteer participants in the group, which included Christopher, represented a range of grades and a variety of KIM roles, across a broad spectrum of departments. The new Framework was launched at a conference in 2016.

The process started with a survey of KIM colleagues, via the departmental Heads of Profession. The results indicated that the existing Framework was seen as too rigid, not user-friendly, with complex language and jargon, and not accessible.

Putting together a plan of action, the working group resolved that the replacement Framework should be flexible, able to fit the profession as it evolves. However, the three main skill areas were retained as definitions: these are (a) abilities to use, evaluate and exploit knowledge and information; (b) abilities to acquire, manage and organise knowledge and information; and (c) information governance skills.

The group resolved to define a ‘foundation level’ for KIM skills, appropriate for juniors, and those outside the profession itself who nevertheless need better information and knowledge handling skills. Because people need to be able to benchmark their skills and performance, a self-assessment tool was recommended (showing parallels with what CILIP have done with their PKSB). Finally, the working group was asked to gather examples of good practice and competency within KIM roles.

Six core KIM-professional roles were identified as existing in all departments (and Christopher returned to the ‘onion diagram’ to display these) – they were, the Information Managers, Records Managers, Information Rights Officers, Knowledge Managers, Information Architects and Librarians. The working group members divided up responsibility for gathering examples of good practice for each of these roles, at all levels.

The draft Framework proposals were then circulated to the departmental Heads of Profession and widely consulted on in other ways, and the working group asked for opinions about whether they had managed to meet the needs expressed by the previous survey. The feedback was overwhelmingly positive, but did lead to some minor amendments being made.

GKIM launch and implementation

The new GKIM Framework was officially launched at the 2016 GKIM Conference. The launch was actively promoted to the departmental Heads of Profession, and Civil Service Learning weighed in by enabling a Web presence for the GKIM materials. (It later emerged in discussion that the Framework documentation consists of one over-arching document, and there are add-ons with more detail about each of the core KIM professions identified.)

Karen closed the presentation with a brief look at the Department for Education as a case study. Within the DfE, senior KIM professionals now have a good awareness of the Framework and its supporting documentation, and are committed to rolling it out to departmental colleagues.

The profile of KIM will be promoted through a ‘KIM Learning Month’ (March 2017), and a stand at the DfE departmental ‘fair’ event in October 2016. The KIM strategy will also be linked to DFE’s performance management objectives, and the Permanent Secretary’s Transformation Programme, within which knowledge management has a critical role to play.

Q&A about GKIM

There were questions asked about whether the slide-set would be available for NetIKX members to peruse later (yes, they will be posted in the Members’ Area), also how accessible the GKIM Framework documents were . The answer to this was that the Framework can be downloaded from http://www.cilip.org.uk/government-information-group/working-government/gkim-skills-framework.  David Penfold reported that the July/August 2016 issue of CILIP Update includes an interview with David Elder about the GKIM Skills Framework.

Table group discussions

I confess that my memory of the table group discussions at this meeting are a bit vague. A flip chart was available during the tea break, on which people could write suggestions for discussion questions, and four were written up, though I cannot remember them in detail, even though I contributed one! The arrangement whereby one question was assigned to each of four table groups was not to my liking: I thought several questions were worth talking about, and the division seemed artificial.

One of the table groups looked at how KIM skills should feature in everyone’s development, not just that of ‘information professionals’ – at least, at the level of promoting a core awareness of the issues. An example might be that everyone should have an awareness about information governance.

The point was made that the language around ‘knowledge’ and ‘skillsets’ is too limiting. You could pick up knowledge and maybe skills by attending some workshops and getting a CPD certificate; but organisations need employees to have appropriate behaviours and values around information and knowledge. I suppose examples could be things like habitually paying attention to information security, or sharing knowledge appropriately with other sections rather than hoarding it.

One of the tables (where I was) had explored a number of topics and not limited to professional development, but looking also at general intellectual development in society at large. There had already been mention of basic skills around information and knowledge, and we considered extended definitions of ‘literacy’, such as ‘information literacy’, and in particular the ability to evaluate information sources as to accuracy, relevance and trustworthiness. 2016 seems to have brought some very low points for poor quality and misleading information, in politics and the media particularly. I personally would like to see more critical thinking taught even in childhood.

There was discussion about how some people need only perhaps a basic ‘awareness’ of KIM issues, plus maybe knowledge about whom to approach for further help. Christopher said that at the foundation level of the GKIM Framework, they do talk about ‘Information Awareness’.

With the government moving in the direction of putting pressure on businesses to take on apprentices, it may be apposite to think about what a KIM apprenticeship might look like, perhaps along the lines of the ‘management apprenticeship’ scheme being developed by the Chartered Management Institute.

[Apologies to Conrad and to all readers for the delay in uploading this report.]

Conrad Taylor writes:

The 80th meeting of the Network for Information and Knowledge Exchange (NetIKX) took place on 14 July 2016 on the topic ‘Understanding Networks’ and was addressed by Drew Mackie and David Wilcox, who also took us through some short exercises. The meeting was chaired by Steve Dale, who has worked with Drew and David on a number of projects.

Drew has researched around network analysis. David’s background is as a journalist (Evening Standard) and he has tried to give people a voice within regeneration and urban development issues. They exercise their joined skills typically in projects for community development and social service strengthening.

In my account of the meeting, I do not exactly follow the order in which the points were made. I also offer my own observations. Where those deviate significantly from the narrative, I’ll signal that in indented italics, as here.

The idea of networks
The concept of a network has many possible applications, such as computer networks, but Drew said our focus would be networks in general and how they can be represented visually and thus analysed. Visualisations appeal greatly to Drew, who is by background an architect and illustrator.

There are various ways in which the nature of an organisation or a community can be expressed, e.g. through stories. Network thinking is a more structural approach. In network representation, one typically has some form of blob which represents an entity (such as a person, department, organisation), and lines are drawn between blobs to show that a relationship exists between the entities on either end.

Mindmap diagrams and ‘organograms’ are forms of network diagrams representing hierarchical set-ups, designed to limit the number and kind of connections possible. Others networks are more freeform.

Hierarchical organisation is just one way in which networks can be constrained. Other examples of constrained networks: connections between components in an electronic circuit are anything but random. You cannot travel on the Underground between King’s Cross and Seven Sisters without passing through Finsbury Park. Connections may also be strongly typed: the connectors in a genealogy diagram may indicate ‘was married to’ or ‘was the child of’, and some connections are not possible – you can’t be the mother of your uncle, for example.

‘Anything that can be drawn as a set of nodes and connections is a network,’ said Drew. The nodes could be people – they could be ideas. For the purposes of this workshop, we considered networks where the nodes are people, organisations and institutions: while not being accidental or random, such networks are not particularly constrained.

People who work with networks
Drew identified four kinds of people who may work with networks. These roles are not mutually exclusive and can overlap.

Network Thinkers understand the power of thinking in terms of networks and promote that view, usually applying it to their particular field, such as management or urban design. In economics, he mentioned author Paul Ormerod, who is a visiting professor at the UCL Centre for Decision Making Uncertainty.

Network Thinkers recognise that networks may have been designed for a purpose (‘intentional networks’), or may emerge from a variety of connections and purposes ‘unintentional networks’; the latter have patterns which evolve and change over time.

Network Analysts are probably those most likely to work with formally diagrammed representations of networks. They survey networks to figure out which nodes are more central, which are more on the periphery. For example, someone may not themselves have many links, but they may link key clusters within the overall network and thus play a central role.

For a simple network with up to about 20 nodes it isn’t too difficult to spot these characteristics in a network diagram, but when the diagrams get more complex it is a good idea to use software which not only draws a representation, but can also perform mathematical analyses (as described more below).

Network Builders help networks grow by creating and strengthening connections between other people, not just their own. Often these connections are between people (or organisations) already connected to the ‘builder’, who might also be described as a broker or bridge-builder. In the kind of community building work that David and Drew do, these people are out there in the community and serve a valuable function.

Networkers, Drew defined as people who are trying to build their own network. They may call their contacts ‘a network’, but more properly it is a list of their direct contacts.

Uses of network theory and analysis
Drew mentioned a number of applications for network thinking.

Organisations, partnerships. A prominent use is in management of organisations, e.g. creating networks to optimise the flows of knowledge and information. A more expanded but similar use is to facilitate partnership working between organisations, communities and individuals: this is a major focus of the work which Drew and David do, and Drew promised to give us examples.

Life transitions. Within a project for the Centre for Ageing Better, they are deploying network analysis with a time dimension, showing how a person’s networks of support and friendship and engagement can change as they age. In the example he showed us later, a fictitious aggregated persona had her network connections changed as her husband retired, then died; she compensated for this by joining activity groups in the community, but later her ill health prevented her from attending them. Also changing over time was her relationship to agencies and individuals in the health service.

Space design. As an architect, Drew notes that network theory can be used in urban design, to identify those places that are most central to the structure of and life in a city. Epidemiology uses network theory to understand how infectious diseases spread, and behaviours which have positive or negative health consequences (from jogging to alcoholism).

Military doctrine is defined by NATO as ‘fundamental principles by which military forces guide their actions in support of objectives… authoritative but [requiring] judgment in application.’ Drew said that the US military now talks about ‘fighting networks with networks’. In the US Military Academy at West Point, Virginia there is now a Network Science Center, a multidisciplinary research project for representing and understanding physical, biological and social phenomena through network-analytical approaches (see http://www.usma.edu/nsc/SitePages/About.aspx. Security services, police forces and of course intelligence services also use network analysis.

Some network concepts
Link maxima. There is quite a bit of maths in network theory, but some levels are easy to understand. Consider, for example, the relationship between the number of nodes, and the number of connections possible between them.

My explanation: suppose I have two friends: in network terms we are three nodes (ignoring our other friends for the sake of argument!). I’m friends with Jim, also Anna, but they don’t yet know each other. So Jim has one connection within the network, Anna has one, and I have two. I introduce Jim to Anna; now each of us has two connections, and the maximum number of possible connections between three nodes (three connections) has been reached.

Try drawing a series of simple circle-and-line diagrams, and count the connections possible. With four nodes, each can have up to three connections and the maximum number of connections is six. In a network of six nodes, each can form up to five connections; the total possible number of connections is 15.

There is a general formula; where n = the number of nodes, the maximum number of connections is n times (n minus one), and the total divided by two. With ten nodes, the maximum number of connections is 45. Double the size of the network to 20 nodes, and now 190 links are possible.

Of course, in a real live network, not everyone is connected directly to everyone else; in any case we just wouldn’t have the cognitive ability to maintain so many links. In networks which Drew and David have mapped, the most links any one node directly makes is about 15. But everyone is connected to everyone indirectly through intermediate nodes in the network.

Centrality. Network theory identifies several forms of ‘centrality’, which broadly stated is a measure of which are the most important nodes in a network system. Today, said Drew, we would look at closeness centrality and betweenness centrality.

As I understand it, the most basic kind of centrality measure is ‘degree centrality’, which simply means the number of links each node has. A person with links to 2 others in the network has less degree centrality than someone with 10. But this can be complicated if the links have some directionality. Consider, on Facebook or Twitter someone may have two million incoming links (‘likes’ or ‘follows’) and so is popular, but has few outgoing links and so is not particularly gregarious.

More complex centrality indices use the idea of the ‘length’ of a path between nodes. This is potentially confusing, because the spread of nodes on a network diagram is bound to mean that some connecting lines appear longer than others, but this is not what is meant. Length here is measured as the number of hops it takes to get from one node to another. If A is linked directly to D, and D directly to M, and M directly to Y, and that is the only way to get from A to Y, then the length of the path from A to Y is three hops.

A simple definition of closeness centrality is centrality to the network as a whole. Nodes which have a high closeness score are best placed to spread information across a network, and they also have a good overview of what is happening across the network.

Suppose you have 26 nodes in a network labelled A to Z and you want to calculate the closeness centrality of node M, add up the number of hops it takes to get from M to A, from M to B, from M to C and so on. The sum of all those lengths for M, divided by the total number of nodes, has been called its index of ‘farness’, and its index of ‘closeness’ is simply the inverse of this.

Betweenness centrality notes that some individual nodes are central to different bits of the network: this is common in networks made up of people. We can identify clusters of nodes that hang together, versus clusters weakly linked to the others. You might do this to identify who to lobby, to whom to feed information, to have the most effect on the network. Nodes with high betweenness centrality act as important bridges within the network, but may also be potential single points of failure.

Betweenness centrality is more difficult to compute. Repeating the above example, we would ask how often does node M act as a ‘stepping stone’ on the path between any two other nodes? This concept was introduced by Linton Freeman in 1977 to help identify, in human networks, who in a network has the most influence or control on communication between other people.

Eigenvector centrality measures how well connected a node is to other well-connected nodes, and such nodes generally play a leadership role within the network.

As I understand it, this is a kind of ‘metameasure’ based on already computed centrality indices for the nodes. Connecting to a node with a high closeness or betweenness centrality (a well connected and influential node) counts for more than connecting to one with a low score. You raise your eigenvector centrality score by connecting to as many well-connected people as you can.

Network density. There are various definitions of this metric. Drew thinks the most useful one is, the average number of connections per node within the network. This works for any network size.

Our imagined ‘A to Z’ network has a theoretical maximum of 325 connections, and if they were all active, each node would have 25 links and we could call that situation ‘100% density’. But polling the network, we may find that A actually has 5 connections, B has 7, C has 3, D has 11 and so on.

Clusters and communities. Network analysis software can identify clusters of nodes which tend to hang together. This is not because they share a common characteristic, but because of the place they occupy in the network. The software can then auto-colour those nodes in groups to help you to notice them. Usually these network clusters turn out to have a basis in the nature of real functional links within the community.

One method for detecting hierarchical sets of communities and sub-communities in large networks was developed at the Catholic University of Louvain in Belgium and is called the Louvain Method. It’s available as C++ or Matlab code and is used in social network analysis tools such as NetworkX and Gephi. (See a pretty thorough if dense explanation on Quora at https://www.quora.com/Is-there-a-simple-explanation-of-the-Louvain-Method-of-community-detection

Another approach to cluster detection is able to notice clusters that overlap, and that it would seem is what the Kumu web-based network analysis tool uses.

Types and uses of networks
David now took over the meeting. As a journalist he had noticed how in a community affected by some proposed urban development project, a ‘helicopter view’ might reveal disconnected initiatives across the community; how to join them up, how to overcome the silo mentalities which crystallise around different professions and cliques? Thus he became interested in network thinking.

David showed us a couple of diagrammatic slides originated by Harold Jarche. One, labelled ‘the network learning model’, creates a space between two axes. The vertical axis indicates ‘diversity’ and ranges from ‘structured and hierarchical’ at the bottom to ‘informal and networked’ at the top. The other axis has ‘goal-oriented and collaborative’ at the left and ‘opportunity-driven and cooperative’ at the right. Ranged up the diagram from bottom-left to top-right are three slightly overlapped balloons representing three levels of networks for sharing and learning:

• Work Teams (structured, goal oriented): based inside a formal organisational structure, sharing complex knowledge, driven by deadlines, strong social ties, co-creating learning.
• Communities of Practice (half-way along both axes): spanning shared concerns across organisations, a trusted space to test ideas, people don’t know each other personally, but integrating work and learning.
• Social Networks (informally co-operative, opportunity-driven): high diversity of ideas and opinions such that you might find stuff you hadn’t considered in your task group; weak social ties.

Visualisation and analysis software
I have already mentioned the use of specialised software to help represent networks and to analyse them. After the exercise and a break, Drew returned to this topic. Most network analysis software, he said, has an analytical and heavily mathematical flavour: examples are UciNet and Gephi. But recently, easier-to-use software has appeared and he described three that he and David have used.

• yEd is a free , open-source diagramming package for Linux, Mac and Windows. I have used this myself, but for drawing a particular kind of non-social network diagram: Entity-Relationship Diagrams (ERD) used in database design. According to Drew, yEd also has some ability to analyse network maps.
• Kumu is their current favourite and main recommendation. It is a web-based system, and you can sign up for a basic free account at kumu.io. You can draw network diagrams with Kumu or it will make them from data and do the analysis; it can also hold stacks of attribute information attached to the nodes and the connections, which enables clever searches and filters on the diagram. Drew and David have been combining network analysis with Asset-Based Community Development methodology (of which, more later), and being able to annotate the notes with what assets they bring to the table has been very useful.
• Polinode. Drew described this as ‘a very slick program’, also web-based, and business oriented. It has a built-in survey mechanism which is useful for collecting information about people in your business network and automatically populating the network diagram accordingly.

Example network maps

Readers may want to look at the PDF file of the slides to see the examples described here. The slides can be found online by clicking here… unless you are reading this off paper, in which case the URL is: https://docs.google.com/presentation/d/1peVwMMIMvlzsfz9AWgA5rtVm_JNMzCyl3 7HUxJTYr0o/edit?ts=5784d539#slide=id.g115d229400_2_10

The first example was created through a survey conducted for the Irish Crafts Council, polling designers and makers, suppliers, retailers and agencies in Ballyhoura, South Tipperary, Wexford, Kilkenny and West Cork. It presents as quite a dense diagram with over 400 nodes and an overlapping mesh of connection lines which in places all run into each other so it is hard to distinguish them. The software discovered three major clusters, based on the link patterns. Interestingly, the clusters were based strongly on geographical proximity – it wasn’t the case that jewellers would network with other like craftspeople across the region, for example, but across the crafts, people networked locally and helped each other out.

The study also revealed that economic development agencies had lots of connections; in West Cork in particular, the agency played a leading role in the network. Meanwhile, though the Wexford and Kilkenny cluster showed a very dense pattern of connections, they were mostly connections within cliques of craft workers, and as such were not very influential across the area.

A second example was for a regeneration partnership programme for Berwick upon Tweed; in this diagram, all 50 or so nodes were organisations. Seven, highlighted on the diagram by the software, were major ‘hubs’ with multiple linkages, with the Borough Council as the most central, playing a ‘brokering’ role between the more strategic organisations at the top of the diagram, and the tightly focused local organisations at the bottom.

Within this project, they then compared the network graph with the results of a survey in which each organisation within the network was asked to rate their perception of (a) the skills held by the other organisations, across five categories and (b) resources those organisations also had to offer, across the same five categories. Dramatically, the Borough Council which the network analysis had identified as being ‘most central’ scores spectacularly the worst on both counts! This leads to interesting discussions. So do you pump money and training and resources into the Council as the centre of that network, or bypass them with a new project? (What actually happened was that all Borough Councils in Northumberland were disbanded.)

Kumu again, in detail
Drew was keen to point out that whereas in the past different software tools would have been needed to work on the different phases of the Berwick upon Tweed project, they were able to do it all simultaneously in Kumu. If anyone is interesting in pursuing this, after this session, he suggests that we get a free Kumu account. That will give each of us a Kumu ‘name’, and he suggested he could put up a Kumu site where we could discuss this stuff and experiment.

There are various ways of getting network data into Kumu. You can draw directly to the screen; Drew likes drawing so he appreciates this. Or, you can type commands into a screen terminal. Comma-separated database files (.csv) can also be uploaded. Kumu can ingest spreadsheet files from Google Sheets, and these in turn can be fed from Google Forms, Google’s form-interface web-based input software. Drew also noted that Kumu is planning to introduce its own integrated survey module soon.

Kumu, as already explained, lets you add extra data to nodes, add node attributes, and tag nodes, which makes search and filtering more powerful. Drew also believes that the developers are very responsive and they listen to how people want it to develop.

More examples, fictitious and applied
Drew showed a network map for ‘Slipham’ – a fictitious community which they use for testing ideas and policies. It is populated by the kinds of local people, organisations and services which would be typical for most communities: there’s the General Hospital and a group GP practice, a branch of Age UK, a number of local councillors, a Somali Association, the Rotary Club, the Police, several sports clubs, etc… and a number of individuals who provide bridging functions through their multiple engagements.

The ‘centrality’ measures for the nodes are emphasised on the map by having the more central, better connected nodes displayed as a proportionately larger circle. The circles are coloured – automatically by the software, on the basis of attribute data that has

been entered for each node. Nodes around education are coloured yellow, red signifies health and social care, and blue is socio-political. (How Kumu displays nodes and links can be customised by bits of Cascading Style Sheet coding, as used in Web site design.)

Drew switched to the Slipham map in Kumu itself, online, and demonstrated how each node can have attributes stored ‘within’ it. He also showed some of the ways that a map can be probed, for example by clicking on one node and having the map display only those other nodes directly linked to it as working contacts – or perhaps within two ‘hops’ rather than one. Selecting two nodes at opposite sides of the map, he got Kumu to show the immediate links of each, helping to identify a couple of nodes shared between them, which could be used as conduits for contact or liaison.

Drew demonstrated a network map produced for NHS Education Scotland (NES). The connector lines on here were interesting in three ways. Firstly, they displayed as curved rather than straight; they displayed with three grades of thickness; and they also seemed to indicate directionality, as each line had an arrowhead at just one end.

The purpose of this investigation was to identify sources and flows of information. The thickness of the line indicates the ‘volume’ of flow (an attribute which you can control by adding a value to the data behind the connection), and although Drew did not explain this, the arrowheads clearly indicate the direction of information flow.

Drew used this diagram to warn of an effect in mapping in real life, which is that one often works for a single client within the network (in this case, the NES), who readily provide their own links, and those initially dominate the map. If you want a more thorough picture, you will have to make contact with the organisations they have identified, and survey them to try to ascertain their links too. It may take a few iterations of this process to get the wider picture.

A second exercise (or game)
We had already had a simple discussion exercise about networks in our table groups. Drew now offered something more playful. He used Kumu to present an abstract network map where the nodes were identified by numeral only. Displayed next to this map on the left was a listing of the ‘top ten’ nodes ranked by betweenness centrality. Each table was to ‘adopt’ one node and then try to promote it up the centrality score table, either by adding a link, or deleting one. (The ‘adopted’ node did not have to be a terminus for the link added or deleted.) Based on table choices, Drew input the changes into the Kumu map and re-displayed the rankings. We did this a couple of times.

The game was competitive and fun, and less confusing than it might have been, because three tables adopted the same node, and two another. The biggest effects came from making or breaking links between nodes which were already well connected. This was a good exercise in learning how to ‘read’ a network diagram.

Collecting information for mapping
After the exercise and a coffee break, Drew gave us guidance about how to prepare for network mapping by gathering information. Where a community is dispersed or hard to collect together, you might prepare an online survey; they’d used Google Form, but Survey Monkey also works. The data may have to be fed in via Excel or Google Sheets, and as Comma Separated Values (.csv). London Voluntary Service Council is currently doing a network mapping exercise using online forms.

If you are holding an event where participants are present, you could get people to input data straight into Kumu, or create a drawn-up paper sheet or questionnaire. Drew showed a model: an Organisational Mapping Sheet they had prepared to collect data for a project on tobacco reduction. Each organisation notes their name at the top of the sheet, and adds some ‘interest keywords’ – I guess this is used to sift the nodes into categories, and so would work best with a predetermined tag vocabulary.

The sample illustrated then had a number of small repeated tables, the first of which was for ‘your organisation’. One box asked ‘Sharing?’ – if you think your organisation is good at sharing, you tick it; if not, you leave it blank.

The sample form then listed five rows of activity: Online communications, Technical, Management, Financial and Community, and next to each of this was a box for ‘Skills’ and another for ‘Resources’. If you have technical skills, you tick that box, and if you have financial resources, you tick that. Otherwise, you leave them blank.

The sample sheet shown had seven other mini-tables identical to the first, except that rather than being about ‘your organisation’ this was for your private opinion about the sharing abilities, skills and resources about the other organisations with which yours was most in contact. There was a note to assure people that ‘individual contributions will be confidential and unattributable’.

This is just one example. Depending on the theme and the nature of the community, the skill and resources sets may be different. Instead of a tick or the absence of one, you could calibrate the data with a numerical score, for example a plus or minus figure, or a number between 1 and 5, or a number of ticks. A calibrated assessment seems to have been used in the Berwick upon Tweed case study described earlier.

Other supplementary means for collecting data could be face to face or telephone interviews. If you are ‘iterating’ the investigation by contacting other organisations named in the linking, telephone interviews make a lot of sense unless you have an online form resource and can invite the second-round participants to fill that out too.

Not that difficult!
Drew showed one example of a fairly complex network map encompassing about 70 organisations, which had been created by the manager of the a Children’s Centre in Croydon to indicate those involved in some way in Croydon’s ‘Best Start’ programme, for children under 5 in families at risk in some way. Following a workshop, she went home, created a Kumu account and without previous experience of network mapping created the network map in two hours.

Another advantage of developing a network map in an online environment like Kumu is that Drew was able, as it were, to ‘look over her shoulder’ and help her remotely to develop her network map further.

One problem with a network map created thus by an individual is that, although she thinks those links exist, she doesn’t know for sure, and the links are unqualified in other ways. A maxim in the network mapping community is ‘the node knows’ – best not to speculate but ask people and organisations in a prospective network what their connections really are.

Time-base networks: the CFAB example
Networks can have a time dimension, and Kumu can cope with these too. An example might be a flow-chart, or a process-mapping chart.

Family Maps. As mentioned briefly above, a recent project which Drew and David have tackled is for the Centre for Ageing Better (CFAB), which wanted to investigate how technology can be used to assist people in later life. They started by developing six ‘personas’. A persona is a fictitious person who embodies a set of characteristics typically found together, so can represent a sector of the population in a simulation game such as one that CFAB ran with 50 people at one of their conferences.

These personas were based on research conducted by the polling company IpsosMORI, who have a database of population characteristics from polling, plus focus groups. IpsosMORI had already concluded that three factors dominate in securing well being in later life: financial security, health, and social connections.

Based on IpsosMORI cluster work, the CFAB project created ‘Mary’, whose tagline was ‘Can Do and Connected’. The character was represented by a cartoon portrait by Drew, in which she says ‘I want to remain independent as long as possible!’ Five sentences explain that she is 73, owns her home outright, but feels she has to watch her spending. She recently lost her husband, but stays positive with support from friends and family, and engages in local activities. She has long-term health issues, but hopes things will improve and stays optimistic. She uses an old mobile phone for calls and texts, but her attitude indicates she would explore technology further if she thought it would help…

Mary lives in ‘Slipham’ (of course) and has connections with various agencies there such as the General Hospital, a community nurse and a bowling club, plus several individuals who are friends, children and grandchildren, etc.

For this project (perhaps through the gaming process?) they also developed time-based maps which showed how Mary’s network might evolve over time: she compensated for the loss of her husband by joining community social activities such as ‘PowerAgers’ (a walking group), but later had to give them up due to advancing ill health, which also changed her needs and her network of support from health and social care agencies. For the network mapping in Kumu, this evolution of her network was coded by tagging each node in it for inclusion in various year bands. You can then advance year by year (in this case, in five-year steps) to see how the persona’s network develops.

The purpose of the exercise was to explore how support services might be better co­ordinated to help people as they get older – and the role which technology might play in that. This was allied to investigation of how vulnerable people are to social isolation.

Drew spoke of the phenomenon of ‘social ageing’ – how our social connections change as a result of ageing. A related concept is ‘network risk’, which spots which kinds of contact network are vulnerable to sudden collapse. They tend to be the ones dependent on physical activity – but could also be impacted by poor public transport provision, or financial hardship, meaning that you can no longer afford to participate in activities.

Multi-level maps. Drew showed how for the CFAB exercise they created a custom version of ‘Slipham’. So long as the node entities reside in the same Kumu project, their links and other attributes will be inherited by other maps created within the project. Drew pointed out that a couple of the nodes on Mary’s personal map are also on the wider Slipham map – others, which might be relevant to Mary’s future happiness and well-being (such as Age UK Slipshire and the University of the Third Age), were not.

Linking to Asset Based Community Development projects

Drew’s final slide showed a very complex network map developed around several projects in Croydon, on which he and David are currently working.

I was interested to note that in the Croydon work, the network mapping is part of a larger programme using ABCD methodology – Asset Based Community Development. My awareness of ABCD has come from another community development practitioner, Ron Donaldson – who spoke at the NetIKX#78 event – and who uses ABCD in some of his own work.

Asset Based Community Development is an approach to developing activities and services within communities which focuses not so much on community needs as on the skills, resources and capabilities of individuals and groupings and organisations within a community. The approach was developed in the 1990 by John L McKnight and John P Kretzmann at the Institute for Policy Research at Northwestern University in Illinois, USA. The website of the ABCD Institute which they founded, anchored within the university’s Center for Civic Engagement, is at http://www.abcdinstitute.org/

For example, we may find out that the Methodist Church has a meeting hall, the school has a grassy area suitable for a neighbourhood fair, Darren is a whiz at Web sites, Ant is a cartoonist, Charmaine and Sue make Jamaican patties, Nguyen is a videographer and videomaker with his own kit, three of the gents from Men In Sheds would like to teach basic woodwork, Sarah has a pillar drill and lathe and can weld, Pushpinder creates theatrical costumes, three Green Party activists want to encourage materials recycling, Jordan can drive a minibus… If these assets can be put together in inventive ways, the community can start to help itself rather than waiting on help from on high.

A key tool in ABCD is the Capacity Inventory which gathers data about who has or can do what, and also finds out how they are connected to projects in the community. To me it has now become obvious that rather than a static card index or other capacity inventory database, an interactive network map with data behind it such as Drew had shown us using Kumu fits beautifully with Asset Based Community Development.

Audience feedback
Many people likes the example of ‘Mary’. Steve Dale said that it is not uncommon for our networks to shrink as we get older. Rob Rosset wondered if that is something we accept, or struggle against. Steve felt that maybe the ageing brain is not as able to cope with lots of connections, but thinking about people he knew years ago, such as in his Navy career – well, their paths have diverged from his anyway, and he would rather stay close to a smaller circle of family and friends who mean a lot to him.

Someone else affected by the story of ‘Mary’ thought exclusion and isolation are the other side of networking. She added that many people are not confident and outgoing networkers, so as well as thinking about how to strengthen our networks by building the links between those who link readily, we should also think about those who stand on the sidelines only for lack of encouragement.

Conrad reflected on the game we had played. Some people with try to strengthen their local dominance in a network, whereas if they were less egotistical and were prepared to connect on an equal basis with people at the heart of other clusters, more could be achieved. Drew commented that a network map can identify someone who occupies a strong network position – but that doesn’t guarantee the right constructive attitude!

David Wilcox reported that a concern of the London Voluntary Service Council is that in the current austerity climate, voluntary action is being crippled as the agencies and associations which used to serve as hubs are taken out. How can the existing groups become better at using network thinking and technology? But those organisations rarely have those skills and capabilities.

David has begun thinking it would be good to develop a range of personas which might represent Londoners – as a starting point for examining what kinds of connections they tend to have, and what they might benefit from in the future – either on their own, or assisted by ‘Network Builders’. Because it looks as if increasingly we are going to have to create social infrastructure from the bottom up. He’d be interested to know if anybody else would be interested in that, to get a project going.

Clare Parry thought that people may share a neighbourhood, but the communities don’t connect – the example she offered was of traveller communities not connecting to settled ones (different ethnic and cultural communities would be another case in point). David said that this was a feature of the ABCD work in Croydon which has been going for about four years. They have volunteer ‘community connectors’ and Drew has been using network mapping to help them identify useful points of inter-community connection.

Finally, Martin expressed concerns about the ability of network maps to misrepresent situations if the data input is wrong or insufficient. Drew said that network maps can give you insights – but if you really want to know what’s going on, you have to investigate that in the field.

SOME FINAL THOUGHTS
This was an interesting and well-attended NetIKX meeting. It’s nice when we have use of the Upper Mews Room at the British Dental Association – it accommodates 50 comfortably around tables and is well lit, very suitable for the round-table syndicate groups which are a hallmark of NetIKX meetings. (To learn more about NetIKX, see https://www.netikx.org.)

As one of the early slides said, there are various ways of getting a picture of how things and people are organised, such as through stories. Network analysis is a more structural and structured method. But I think more people are comfortable with stories and I suspect some of my NetIKX colleagues felt they had waded beyond their depth when we tackled network theory! This may be why the story of ‘Mary’ resonated so well – however fictitious, there was a story in there. The stories around other projects such as the Berwick upon Tweed one also helped bring these to life for me.

I was intrigued enough by betweenness metrics and other abstract aspects of network theory to do more reading around them, if only to help explain them. I hope my expanded explanations of how these things work with reference to my fictitious abstract ‘A to Z’ network are (a) helpful and (b) not misleading!

Obviously there are subtleties of social network analysis and visualisation which we didn’t cover (and which could have led to rapid cognitive overload has it been attempted). For example: the directionality of links; the strength of links; how if at all to weight the value of a particular node’s contribution to the network. I look forward to playing with Kumu to discover more and I have signed up, as suggested by Drew.

On the CFAB example and social ageing —The story of Mary made me think too. Many people of my age (early 60s) and even decades older find that the Internet and social media, even Facebook plus digital photography, help us keep in contact with friends dispersed across the globe, people whom we meet face to face but rarely, and even make new friends by being introduced online to friends of friends. Quite cost effectively, and even if we are housebound.

Writing and reading – perhaps falling out of fashion? – can network us with others in great depth. Frequent emails are important to my mother and me. Text can link us to ideas across time as well as space, for example by reading books – or accounts like this of interesting meetings we may have missed…

In Mary’s story of ageing, illness decreased her access to wider networks, but that is not the only factor. There are many activities I cannot join for lack of funds. I cannot begin to express how grateful I am to have a 60+ Oystercard and therefore free travel across London!