An agent-based model of communications traffic generated within a social network is described, which caters for various knowledge discovery techniques to be used in order to extract 'interesting' temporal patterns contained within anomalous data records. Attendant Java-based software — NetShow — is presented which enables analysis and display of static network configuration, links between network nodes, and correlations between the traffic passing through nominated nodes. In addition, display of network dynamics is facilitated via the incorporation of swarm techniques. Related issues of 'similarity', 'familiarity' and 'contact lists' are discussed within the context of Social Network Analysis and Link Mining. Finally, several suggestions are made for extending the work reported earlier in the chapter.
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