Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Computer Science Review
Structure and attributes community detection benchmark and a novel selection method
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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If the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the vertices and attributes describing them, is quite new. In this paper, we present different scenarios for this task and, we evaluate their performances and their results on a dataset, with ground truth, built from several sources and containing a scientific social network in which textual data is associated to each vertex and the classes are known. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this.