Conference Proceedings

TRIBAC: Discovering Interpretable Clusters and Latent Structures in Graphs

J Chan, C Leckie, J Bailey, R Kotagiri, R Kumar (ed.), H Toivonen (ed.), J Pei (ed.), JZ Huang (ed.), X Wu (ed.)

Data Mining (ICDM), 2014 IEEE International Conference on | IEEE | Published : 2014

Abstract

Graphs are a powerful representation of relational data, such as social and biological networks. Often, these entities form groups and are organised according to a latent structure. However, these groupings and structures are generally unknown and it can be difficult to identify them. Graph clustering is an important type of approach used to discover these vertex groups and the latent structure within graphs. One type of approach for graph clustering is non-negative matrix factorisation However, the formulations of existing factorisation approaches can be overly relaxed and their groupings and results consequently difficult to interpret, may fail to discover the true latent structure and gro..

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