Finding interesting trends in social networks using frequent pattern mining and self organizing maps

This paper introduces a technique that uses frequent pattern mining and SOM techniques to identify, group and analyse trends in sequences of time stamped social networks so as to identify interesting trends. In this study, trends are defined in terms of a series of occurrence counts associated with frequent patterns that may be identified within social networks. Typically a large number of frequent patterns, and by extension a large number of trends, are discovered. Thus, to assist with the analysis of the discovered trends, the use of SUM techniques is advocated so that similar trends can be grouped together. To identify interesting trends a sequences of SOMs are generated which can be interpreted by considering how trends move from one SUM to the next. The further a trend moves from one SUM to the next, the more interesting the trend is deemed to be. The study is focused two types of network, Star networks and Complex star networks, exemplified by two real applications: the Cattle Tracing System in operation in Great Britain and a car insurance quotation application. (C) 2011 Published by Elsevier B.V.