Frequent Pattern Trend Analysis in Social Networks

Abstract

This paper describes an approach to identifying and comparing frequent pattern trends in social networks. A frequent pattern trend is defined as a sequence of time-stamped occurrence (support) values for specific frequent patterns that exist in the data. The trends are generated according to epochs. Therefore, trend changes across a sequence epochs can be identified. In many cases, a great many trends are identified and difficult to interpret the result. With a combination of constraints, placed on the frequent patterns, and clustering and cluster analysis techniques, it is argued that analysis of the result is enhanced. Clustering technique uses a Self Organising Map approach to produce a sequence of maps, one per epoch. These maps can then be compared and the movement of trends identified. This Frequent Pattern Trend Mining framework has been evaluated using two non-standard types of social networks, the cattle movement network and the insurance quote network.