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 SOM 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 SOM to the next. The further a trend moves from one SOM 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.

► We discovered temporal frequent patterns and trends using a Trend Mining technique.
► The technique processed time-stamped social network datasets.
► Two types of network were discovered, Star and Complex Star networks.
► We clustered the identified patterns and trends so as to discover “interesting” trends.
► Trend analysis was applied to highlight dynamic changes in the discovered patterns and trends.