Self-Organizing map and tree topology for graph summarization

Authors:
Nhat-Quang Doan;Hanane Azzag;Mustapha Lebbah
Affiliations:
Laboratoire d’Informatique de Paris-Nord (LIPN), CNRS (UMR 7030), Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France;Laboratoire d’Informatique de Paris-Nord (LIPN), CNRS (UMR 7030), Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France;Laboratoire d’Informatique de Paris-Nord (LIPN), CNRS (UMR 7030), Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

In this paper, we present a novel approach called SOM-tree to summarize a given graph into a smaller one by using a new decomposition of original graph. The proposed approach provides simultaneously a topological map and a tree topology of data using self-organizing maps. Unlike other clustering methods, the tree-structure aim to preserve the strengths of connections between graph vertices. The hierarchical nature of the summarization data structure is particularly attractive. Experiments evaluated by Accuracy and Normalized Mutual Information conducted on real data sets demonstrate the good performance of SOM-tree.