The rise of on-line social network platforms such as Facebook has made the general population more network-aware. Yet, at the same time, this obscures the many other ways in which network concepts and analysis can be of use. Network Science was billed as the topic for the November 2018 NetIKX seminar, and in hopes that we would explore the topic widely, I did some preliminary reading.
I find that Network Science is perhaps not so much a discipline in its own right, as an approach with application in many fields – analysis of natural and engineered geography, transport and communication, trade and manufacture, even dynamic systems in chemistry and biology. In essence, the approach models ‘distinct elements or actors represented by nodes (or vertices) and the connections between [them] as links (or edges)’ (Wikipedia), and has strong links to a branch of mathematics called Graph Theory, building on work by Euler in the 18th century.
In 2005, the US National Academy of Sciences was commissioned by the US Army to prepare a general report on the status of Network Science and its possible application to future war-fighting and security preparedness: the promise was, that if the approach looked valuable, the Army would put money into getting universities to study the field. The NAS report is available publicly at http://nap.edu/11516 and is worth a read. It groups the fields of application broadly into three: (a) geophysical and biological networks (e.g. river systems, food webs); (b) engineered networks (roads, electricity grid, the Internet); and (c) social networks and institutions.
I’ve prepared a one-page summary, ‘Network Science: some instances of networks and fields of complex dynamic interaction’, which also lists some further study resources, five books and an online movie. (Contact NetIKX if you want to see this). In that I also note: ‘We cannot consider the various types of network… to be independent of each other. Amazon relies on people ordering via the Internet, which relies on a telecomms network, and electronic financial transaction processing, all of which relies on the provision of electricity; their transport and delivery of goods relies on logistics services, therefore roads, marine cargo networks, ports, etc.’
The NetIKX seminar fell neatly into two halves. The first speaker, Professor Yasmin Merali of Hull University Business School, offered us a high-level theoretical view and the applications she laid emphasis on were those critical to business success and adaptation, and cybersecurity. Drew Mackie then provided a tighter focus on how social network research and ‘mapping’ can help to mobilise local community resources for social welfare provision.
Drew’s contribution was in some measure a reprise of the seminar he gave with David Wilcox in July 2016. Another NetIKX seminar which examined the related topics of graph databases and linked data graphs is that given by Dion Lindsay and Dave Clarke in January 2018.
Yasmin Merali noted that five years ago there wasn’t much talk about systems, but now it is commonplace for problems to be identified as ‘systemic’. Yet, ironically, Systems Thinking used to be very hot in the 1990s, later displaced by a fascination with computing technologies. Now once again we realise that we live in a very complex and increasingly unpredictable world of interactions at many levels; where the macro level has properties and behaviours that emerge from what happens at the micro level, without being consciously planned for or even anticipated. We need new analytical frameworks.
Our world is a Complex Adaptive System (CAS). It’s complex because of its many interconnected components, which influence and constrain and feed back upon each other. It is not deterministic like a machine, but more like a biological or ecological system. Complex Adaptive Systems are both stable (persistent) and malleable, with an ability to transform themselves in response to environmental pressures and stimuli – that is the ‘adaptive’ bit.
We have become highly attuned to the idea of networks through exposure to social media; the ideas of ‘gatekeepers’, popularity and influence in such a network are quite easy to understand. But this is selling short the potential of network analysis.
In successful, resilient systems, you will find a lot of diversity: many kinds of entity exist and interact within them. The links between entities in such systems are equally diverse. Links may persist, but they are not there for ever, nor is their nature static. This means the network can be ‘re-wired’, which makes adaptation easier.
Amazing non-linear effects can emerge from network organisation, and you can exploit this in two ways. If adverse phenomena are encountered, the network can implement a corrective feedback response very quickly (for example, to isolate part of the network, which is the correct public health response in the case of an epidemic). Or, if that reaction isn’t going to have the desired effect, we can try to re-wire the network, dampening some feedback loops, reinforcing others, and thus strengthening those ‘constellations’ of links which can best rise to the situation.
Information flows in the network. Yasmin offered us as analogy, the road network system, and distinct to that, the traffic running across that network. People writing about the power of social media have been concentrating on the network structure (the nodes, and the links), but not so much on the factors which enable or inhibit different kinds of dynamic within that structure.
Networks can enable efficient utilisation of distributed resources. We can also see networks as the locus where options are generated. Each change in a network brings about new conditions. But the generative capacity does come at a cost: you must allow sufficient diversity. Even if there are elements which don’t seem useful right now, there is a value in having redundant components: that’s how you get resilience.
You might extend network thinking outwards, beyond networking within one organisation, towards a number of organisations co-operating or competing with each other. Some of your potential partners can do better in the current system and with their resources than you; in another set of circumstances, it might be you who can do better. If we can co-operate, each tackling the risks we are best able to cope with, we can spread the overall risk and increase the capability pool.
Yasmin referred to the idea of ‘Six Degrees of Separation’ – that through intermediate connections, each of us is just six link-steps away from anybody else. The idea was important in the development of social network theory, but it turns out to have severe limitations, because where links are very tenuous, the degree of access or influence they imply can be illusory. That’s why simplistic social network graphs can be deceptive.
In a regular ‘small worlds’ network, everyone is connected to the same number of people in some organised way, and even one extra random link shortens the path length. It’s possible to ‘re-wire’ a network to get more of these small-world effects, with the benefit of making very quick transitions possible.
But there is another kind of network, similar in structure to the Internet and most of the biological systems we might consider – and that’s what we can call the ‘scale-free’ network. In this case, there is no cut-off limit to how large, or how well-connected a node can be.
Networks are also ‘lumpy’ – in large networks, there are very large hubs, but also adjacent less-prominent hubs, which in an Internet scenario are less likely to be attacked or degraded. This gives some hope that the system as a whole is less likely to be brought to its knees by a random attack; but a well-targeted attack against the larger hubs can indeed inflict a great deal of damage. This is something that concerns security-minded designers of networks for business. It is strategically imperative to have good intelligence about what is going on in a networked system – what are the entities, which of them are connected, and what is the nature of those connections and the information flows between them.
It’s important to distinguish between resilience and robustness. Resilience often comes from having network resources in place which may be redundant, may appear to be superfluous or of marginal value, but they provide a broader option space and a better ability to adapt to changing circumstance.
Looking more specifically at social networks, Yasmin referred to the ‘birds of a feather flock together’ principle, where people are clustered and linked based on similar values, aspirations, interests, ways of thinking etc. Networks like this are often efficient and fast to react, and much networking in business operates along those lines. However, within such a network, you are unlikely to encounter new, possibly valuable alternative knowledge and ways of thinking.
Heterogeneity of linkages may propagate along weaker links, but are valuable for expanding the knowledge pool. Expanded linkages may operate along the ‘six degrees’ principle, and through intermediate friends-of-friends, who serve both as transmitters and as filters. And yet a trend has been observed for social network engines (such as Facebook) to create a superdominance of ‘birds of a feather’ types of linkages, leading to confirmation bias and even polarisation.
In traditional ‘embodied’ social networks, people bonded and transacted with others whom they knew in relatively persistent ways, and could assess through an extended series of interactions in a broadly understandable context. In the modern cybersocial network, this is more difficult to re-create, because interactions occur through ‘shallow’ forms such as text and image – information is the main currency – and often between people who do not really know each other.
Another problem is the increased speed of information transfer, and decreased threshold of time for critical thought. Decent journalism has been one of the casualties. Yes, ‘citizen journalism’ via tweet or online video post can provide useful information – such informants can often go where the traditional correspondent could not – but verification becomes problematic, as does getting the broader picture, when competition between news channels to be first with the breaking story ‘trumps’ accuracy and broader context.
If we think of cybersocial networks as information networks, carrying information and meaning, things become interesting. Complexity comes not just from the arrangement of links and nodes, but also from the multiple versions of information, and whether a ‘message’ means the same to each person who receives it: there may be multiple frameworks of representation and understanding standing between you and the origin of the information.
This has ethical implications. Some people say that the Internet has pushed us into a new space. Yasmin argues that many of the issues are those we had before, only now more intensely. If we think about the ‘gig economy’, where labour value is extracted but workers have scant rights – or if we think about the ownership of data and the rights to use it, or surveillance culture – these issues have always been around. True, those problems are now being magnified, but maybe that cloud has a silver lining in forcing legislators to start thinking about how to control matters. Or is it the case that the new technologies of interaction have embedded themselves at such a fundamental level that we cannot shift them?
What worries Yasmin more are issues around Big Data. As we store increasingly large, increasingly granular data about people from sources such as fitbits, GPS trackers, Internet-of-Things devices, online searches… we may have more data, but are we better informed? Connectivity is said to be communication, but do we understand what is being said? The complexity of the data brings new challenges for ethics – often, you don’t know where it comes from, what was the quality of the instrumentation, and how to interpret the data sets.
And then there is artificial intelligence. The early dream was that AI would augment human capability, not displace it. In practice, it looks as if AI applications do have the potential to obliterate human agency. Historically, our frameworks for how to be in the world, how to understand it, were derived from our physical and social environment. Because our direct access to the physical world and the raw data derived from it is compromised, replaced by other people’s representation of other people’s possible worlds, we need to figure out whose ‘news’ we can trust.
When we act in response to the aggregated views of others, and messages filtered through the media, we can end up reinforcing those messages. Yasmin gave as an example rumours of the imminent collapse of a bank, causing a ‘bank run’ which actually does cause the bank’s collapse (in the UK, an example was the September 2007 run on Northern Rock). She also recounted examples of the American broadcast media’s spin on world events, such as the beginning of the war in Iraq, and 9/11. People chose to tune into to those media outlets whose view of the world they preferred. (‘Oh honey, why do you watch those channels? It’s so much nicer on Fox News.’
There is so much data available out there, that a media channel can easily find provable facts and package them together to support its own interpretation of the world. This process of ‘cementation’ of the silos makes dialogue between opposed camps increasingly difficult – a discontinuity of contemporaneous worlds. This raises questions about the way our contextual filtering is evolving in the era of the cybersocial. And if we lose our ‘contextual compass’, interpreting the world becomes more problematic.
In Artificial Intelligence, there are embedded rules. How does this affect human agency in making judgements? One may try to inject some serendipity into the process – but serendipity, said Yasmin, is not that serendipitous.
Yasmin left us with some questions. Who controls the network, and who controls the message? Should we be sitting back, or are their ethical considerations that mean we should be actively worrying about these things and doing what we can? What is it ethical not to have known, when things go wrong?
Drew Mackie prepares network maps for organisations; most of the examples he would give are in the London area. He declared he would not be talking about network theory, although much is implied, and underlies what he would address.
Mostly, Drew and his associates work with community groups. What they seek to ‘map’ are locally available resources, which may themselves be community groups, or agencies. In this context, one way to find out ‘where stuff is’ is to consult some kind of catalogue, such as those which local authorities prepare. And a location map will show you where stuff is. But when it comes to a network map, what we try to find out and depict is who collaborates with whom, across a whole range of agencies, community groups, and key individuals.
When an organisation commissions a network map from Drew, they generally have a clear idea of what they want to do with it. They may want to know patterns of collaboration, what assets are shared, who the key influencers are, and it’s because they want to use that information to influence policy, or to form projects or programmes in that area.
Drew explained that the kinds of network map he would be talking about are more than just visual representations that can be analysed according to various metrics. They are also a kind of database: they hold huge amounts of data in the nodes and connections, about how people collaborate, what assets they hold, etc. So really, what we create is a combination of a database and a network map, and as he would demonstrate, software can help us maintain both aspects.
If you want to build such a network map, it is essentially to appoint a Map Manager to control it, update it, and also promote it. Unless you generate and maintain that awareness, in six months the map will be dead: people won’t understand it, or why it was created.
Residents in the area may be the beneficiaries, but we don’t expect them to interact with the map to any great extent. The main users will be one step up. To collect the information that goes into building the map, and to encourage people to support the project, you need people who act as community builders; Drew and his colleagues put quite a lot of effort in training such people.
To do this, they use two pieces of online software: sumApp, and Kumu. SumApp is the data collection program, into which you feed data from various sources, and it automatically builds you a network map through the agency of Kumu, the network visualisation and analytics tool. Data can be exported from either of these.
When people contribute their data to such a system, what they see online is the sumApp front end; they contribute data, then they get to see the generated network map. No-one has to do any drawing. SumApp can be left open as a permanent portal to the network map, so people can keep updating their data; and that’s important, because otherwise keeping a network map up to date is a nightmare (and probably won’t happen, if it’s left to an individual to do).
The information entered can be tagged with a date, and this allows a form of visualisation that shows how the network changes over time.
Drew then showed us how sumApp works, first demonstrating the management ‘dashboard’ through which we can monitor who are the participants, the number of emails sent, connections made and received, etc. So that we can experience that ourselves should we wish, Drew said he would see about inviting everyone present to join the demonstration map.
Data is gathered in through a survey form, which can be customised to the project’s purpose. To gather information about a participant’s connections, sumApp presents an array of ‘cards’, which you can scroll through or search, to identify those with whom you have a connection; and if you make a selection, a pop-up box enquires how frequently you interact with that person – in general, that correlates well with how closely you collaborate – and you can add a little story about why you connect. Generally that is in words, but sound and video clips can also be added.
Having got ‘data input’ out of the way, Drew showed us how the map can be explored. You can see a complete list of all the members of the map. If you were to view the whole map and all its connections, you would see an undecipherable mess; but by selecting a node member and choosing a command, you can for example fade back all but the immediate (first-degree) connections of one node (he chose our member Steve Dale as an example). Or, you could filter to see only those with a particular interest, or other attribute in common.
Drew also demonstrated that you can ask to see who else is connected to one person or institution via a second degree of connection – for example, those people connected to Steve via Conrad. This is a useful tool for organisations which are seeking to understand the whole mesh of organisations and other contacts round about them. Those who are keenest in using this are not policy people or managers, but people with one foot in the community, and the other foot in a management role. People such as children’s centre managers, or youth team leaders – people delivering a service locally, but who want to understand the broader ecology…
Kumu is easy to use, and Drew and colleagues have held training sessions for people about the broad principles, only for those people to go home and, that night, draw their own Kumu map in a couple of hours – not untypically including about 80 different organisations.
Drew also demonstrated a network map created for the Centre for Ageing Better (CFAB). With the help of Ipsos MORI, they had produced six ‘personas’ which could represent different kinds of older people. One purpose of that project was to see how support services might be better co-ordinated to help people as they get older. Because Drew also talked through this in the July 2016 NetIKX meeting, I shall not cover it again here.
Drew also showed an example created in Graph Commons (https://graphcommons.com/). This network visualisation software has a nice feature that lets you get a rapid overview of a map in terms of its clusters, highlighting the person or organisation who is most central within that cluster, aggregating clusters for viewing purposes into a single higher-level node, and letting you explore the links between the clusters. The developers of sumApp are planning a forthcoming feature that will let sumApp work with Graph Commons as an alternative graph engine to Kumu.
In closing, Drew suggested that as a table-group exercise we should discuss ideas for how these insights, techniques and tools might be useful in our own work situations; note these on a sheet of flip-chart paper; and then we could later compare the outputs across tables.