Social Network Trend Analysis Using Frequent Pattern Mining and Self Organizing Maps

Abstract

A technique for identifying, grouping and analysing trends in social networks is described. The trends of interest are defined in terms of sequences of support values for specific patterns that appear across a given social network. The trends are grouped using a SOM technique so that similar trends are clustered together. A cluster analysis technique is then applied to identify "interesting" trends. The focus of the paper is the Cattle Tracing System (CTS) database in operation in Great Britain, and this is therefore the focus of the evaluation. However, to illustrate the wider applicability of the trend mining technique, experiments using a more standard, car insurance, temporal database are also described.

Trend Mining and Visualisation in Social Networks

“…There is no certain scientific method [5] to specify the optimum value for n × m for how many clusters should be presented in SOM. A 10 × 10 node SOM was chosen as a result of earlier experiments repeated in [16,18].…”

A fast and resource efficient mining algorithm for discovering frequent patterns in distributed computing environments

“…With the rise of social networking, a user's preferences or likes can be known by analysing the articles posted or shared by the user, by their 'likes' and by their subscriptions to fan pages. The advertising impact can be improved by mining the attention of customers [15,27]. Some of the past studies developed mining algorithms for mining useful information from gene-expression data [5,11,31] to help with the new direction of human diseases.…”