Parallel multilevel k-way partitioning scheme for irregular graphs
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical models for partial membership
Proceedings of the 25th international conference on Machine learning
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Subgraph mining on directed and weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Traditional graph-based clustering methods group vertices into non-intersecting clusters under the assumption that each vertex can belong to only a single cluster. On the other hand, recent research on graph-based clustering methods, applied to real world networks (e.g., protein-protein interaction networks and social networks), shows overlapping patterns among the underlying clusters. For example, in social networks, an individual is expected to belong to multiple clusters (or communities), rather than strictly confining himself/herself to just one. As such, overlapping clusters enable better models of real-life phenomena. Soft clustering (e.g., fuzzy c-means) has been used with success for network data as well as non-graph data, when the objects are allowed to belong to multiple clusters with a certain degree of membership. In this paper, we propose a fuzzy clustering based approach for community detection in a weighted graphical representation of social and biological networks, for which the ground truth associated to the nodes is available. We compare our results with a baseline method for both multilabeled and single-labeled datasets.