DATA ANALYSIS: SOCIAL NETWORKS
Claim-specific citation network, from Greenberg, on distortions and unfounded authority in medical literature
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ago, and in a recent issue of INSNA journal Connections Alan Ellis deals with a particular use of SAS for the calculation of betweenness centrality. Betweenness centrality is a measure used to represent the degree of potential significance to network interaction (in various forms) of an actor who occupies a position on geodesics connecting other actors. The algorithm Ellis presents could, as he points out, be generalised to a number of other measures, but examination of its structure shows that it could also be implemented in other language- based statistical software environments. Two colleagues have, by way of illustration, sketched out in principle how it might be ported to GenStat, Statistica or Systat. Taking a straw poll of mathematical epidemiologists who happened to cross my path, Maple seems to be widely used for their SNA purposes while military and police intelligence analysts tend to use Mathematica. Those are only generalisations, however; there is a lot of overlap, and both groups contain a lot of MatLab fans as well. The best-known police use of social network
data is the data mining of ANPR (automatic number plate recognition) from heterogenous CCTV sources in the UK for associational links. As one officer put it to me: ‘We are usually less interested in who is going where than who visits whom and who consistently travels with whom.’ Vehicles of interest are nodes. Two such vehicles that travel a particular route in tandem, or visit the same locations, are connected by an edge. Geodesics formed from the edges, or cliques that share edges, are flagged and analysed for what they may reveal of underlying associations.
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There is increasing talk in the police (and
the security services, and the military, both of which have access to ANPR databases for counterterrorism purposes) of extending the same system to embrace other types of node. A vehicle, whether identified by number or chip, can be driven by anyone, so nodes that correspond to individuals represent far higher data quality and precision. Facial recognition systems that can scan crowds are not at the capacity level of ANPR as yet, but are progressing. Biometric passports provide data at a much lower spatial resolution, biometric identity cards potentially much higher. Devices other than vehicles, such as mobile phones and bank cards, fill out the picture. Fingerprint and other biometric logging or
access systems provide further input, and not only for policing. One head teacher described to me how SNA methods applied to an automated register system led to a ‘protection racket’ run within a school by bullies being identified and broken up. The same head
also mentioned discipline of a staff member for ethically questionable analysis of complex friendship patterns among pupils. The technological facilitation of social interaction makes some types of SNA particularly easy to tackle. Mobile phone monitoring was mentioned above, but less Orwellian uses for them can be found. One academic is currently monitoring dozens of voluntary GPS-tracked mobile phones and their itemised bills belonging to students who have signed up to a collective study of their own cohort. Another is conducting experiments using unique meme transmission through a large (greater than 10,000 nodes) student body as a network revelation tracer mechanism. A quick and cursory analysis of spam and chain emails turning up on my system reveals strong associational networks among thousands of people (or, more accurately, their email and IP addresses) of whom I have never heard. Social network systems of the Facebook or Twitter variety reveal nodes, edges and all the rest to anyone who cares to look. One of the networks most easily visible
to any researcher is that of interrelated citations, and they have attracted numerous studies with concerns as wide as analysis of differences between Chinese and Western academic systems or as narrow as collaboration within a faculty or even seminar group, with exploration of impacts by a specific field of research (for example, human information behaviour) falling somewhere between the two. Systems such as Delicious or Elsevier’s 2collab provide ready material for this sort of investigation. From origination in sociology to application
in widely disparate fields, social network analysis underpinned by every growing, ever cheaper and ever more ubiquitous computing infrastructures is remarkably versatile in providing scientific means of investigation for a whole raft of disciplines. That range is already impossible to summarise, and looks set to grow exponentially. SNA as a tool of metaresearch into research process is also expanding; an interesting study would be the social network analysis of the cross disciplinary links wrought by SNA itself.
References and sources
A Wolfram demonstration, by Phillip Bonacich, of clique location in networks
For a full list of the sources and references cited in this article, please visit
www.scientific-computing.com/features/ referencesjun10.php
SCIENTIFIC COMPUTING WORLD JUNE/JULY 2010
www.scientific-computing.com
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