DATA ANALYSIS: SOCIAL NETWORKS
Two different illustrations of network relations among elephants at Chester Zoo, studied by Amelia Coeling. On the left, a family tree showing two separate consanguineal subsets. On the right, a weighted mapping of social play
meerkat. The population here contains five interacting tribes comprising a total of 110 ‘actors’ (for a short glossary of terms such as actor, see box: Lexicon), the network recording five types of interaction both within and between groups. From the study emerge indicators suggesting that: ‘Contrary to predictions, the most socially interactive animals were not at highest risk... type and direction of interactions must be considered.’ In particular, ‘meerkats that groomed others most were more likely to become infected than individuals who received high levels of grooming... receiving, but not initiating, aggression was associated with M. bovis infection’.
Epidemiology is, in general, a fruitful area
of application for SNA. One group in Eire is in the early stages of investigating the application of methods similar to Drew’s across the boundary between a wild population and a captive one in investigation of hypothesised transmission of TB from badgers to farmed cattle. Martínez-López et al discuss the use of SNA in preventative veterinary medicine. And epidemiology brings me neatly back to human populations, where SNA is applied to exploration of specific geometries of disease distribution and propagation.
SNA originated as a means for application
of scientific computing to quantitative sociological analysis, and remains of major importance there. It sees heavy use in studies of business models and strategies, but is also applied intensively to military, criminological and antiterrorist studies. It also sees service across a remarkable range of other fields too numerous to even summarise – including areas of human/animal interaction such as farming and, alongside other scientific computing- based methods such as agent-based modelling and fractal analysis, animal welfare. The hardcore social networking analysis fraternity have particular software tools that they favour. ‘UCINet is the dominant app, with a lot written in R,’ says Barry Wellman (see box: Barry Wellman and INSNA), while ‘ORA is used heavily by the intelligence and law enforcement community’. Further out, however, the same methods are being implemented using a wide variety of other scientific computing software. Other network study products, not specifically aimed at SNA, are often used. Generic computer algebra
suites are popular too. Among high-level programming languages, several have their adherents and LISP seems to be particularly favoured. Statistics packages, especially the heavyweights, can do the analysis although they may well not have built in tools for graphical networking display. All of these alternatives are also widely used in tandem with specific SNA software – Madden et al, for example, describe use of both UCINet and GenStat to study different aspects of interaction within a meerkat population. UCINet and ORA offer all the obvious advantages of dedicated and integrated study environments for those whom the majority of study time is spend in SNA-related activity. Both handle large numbers of nodes, though the theoretical maxima are in practice limited by rapidly slowing execution and memory consumption as complexity increases – expect to invest in hardware power if you are planning to use either to the full. The applicability of generic network
analysis products is obvious, especially for anyone coming to SNA in collaboration with colleagues having a previous network background in a different area – and much the same is true of many modelling systems. This is the case with one group of medical researchers who, exploring impacts of consanguinity on a range of health issues in a small isolated community, have recruited a passing agronomist who is building virtual worlds for them in Simile, the modelling program from Simulistics. Simile is not designed for such work, but does provide building blocks for the core concepts together with powerful tools for rapidly controlling and investigating complex scenarios with varying edge weights. Wellman himself coauthored a paper on the application of SPSS, just over a decade ➤
Barry Wellman and INSNA
Interdisciplinary analysis, by Bellanca, of coauthorship by research focus at the University of York
Barry Wellman is a sociologist by background, and a faculty member of more than 30 years standing in that discipline at the University of Toronto, but works intensively with information and communication technology (ICT) specialists. He played a central rôle in the development of SNA methods, and has focused his interest on those social networks that are facilitated by technological ones such as the internet. He has, as one commentator put it, ‘devoted an entire career to exploring and documenting natural social worlds in
www.scientific-computing.com
network terms’. A prominent feature of his research is the ways in which ICT mediation is changing the relation of social networks to physical loci. Wellman founded the field’s main
professional association, the International Network for Social Network Analysis (INSNA) which, among other things, serves as a focus for SNA work and workers, maintains a ListServ discussion forum, and publishes Connections, a peer-reviewed online journal whose archive is available for public access.
SCIENTIFIC COMPUTING WORLD JUNE/JULY 2010
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