Detecting Communities in Social Networks Using Max-Min Modularity.

Many datasets can be described in the form of graphs or networks where nodes in the graph represent entities and edges represent relationships between pairs of entities. A common property of these networks is their community structure, considered as clusters of densely connected groups of vertices, with only sparser connections between groups. The identification of such communities relies on some notion of clustering or density measure. which defines the communities that can be found. However, previous community detection methods usually apply the same structural measure on all kinds of networks, despite their distinct dissimilar features. In this paper, we present a new community mining measure, Max-Min Modularity, which considers both connected pairs and criteria defined by domain experts in finding communities, and then specify a hierarchical clustering algorithm to detect communities in networks. When applied to real world networks for which the community structures are alread...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Jiyang Chen, Osmar R. Zaïane, Randy Goebel
• Keywords: Community Mining Measure, Computer Science, Hierarchical Clustering Algorithm, Real World Networks, SDM 2009