A Fully Automated Method for Discovering Community Structures in High Dimensional Data

—Identifying modules, or natural communities, in large complex networks is fundamental in many fields, including social sciences, biological sciences and engineering. Recently several methods have been developed to automatically identify communities from complex networks by optimizing the modularity function. The advantage of this type of approaches is that the algorithm does not require any parameter to be tuned. However, the modularity-based methods for community discovery assume that the network structure is given explicitly and is correct. In addition, these methods work best if the network is unweighted and/or sparse. In reality, networks are often not directly defined, or may be given as an affinity matrix. In the first case, each node of the network is defined as a point in a high dimensional space and different networks can be obtained with different network construction methods, resulting in different community structures. In the second case, an affinity matrix may de�...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Jianhua Ruan
• Keywords: Community Structure, Complex Networks, Data Mining, ICDM 2009, Network Construction Method