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 multi-labeled and single-labeled datasets. © 2011 Springer-Verlag.
CITATION STYLE
Saha, T., Domeniconi, C., & Rangwala, H. (2011). Detection of communities and bridges in weighted networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6871 LNAI, pp. 584–598). https://doi.org/10.1007/978-3-642-23199-5_43
Mendeley helps you to discover research relevant for your work.