Finding Frequent Subgraphs in Longitudinal Social Network Data Using a Weighted Graph Mining Approach

摘要

The mining of social networks entails a high degree of computational complexity. This complexity is exacerbate when considering longitudinal social network data. To address this complexity issue three weighting schemes are proposed in this paper. The fundamental idea is to reduce the complexity by considering only the most significant nodes and links. The proposed weighting schemes have been incorporated into the weighted variations and extensions of the well established gSpan frequent subgraph mining algorithm. The focus of the work is the cattle movement network found in Great Britain. A complete evaluation of the proposed approaches is presented using this network. In addition, the utility of the discovered patterns is illustrated by constructing a sequential data set to which a sequential mining algorithm can be applied to capturing the changes in "behavior" represented by a network.