Community Detection in Complex Networks

As a ubiquitous feature of complex networks, community characteristic is common in real world networks especially in wireless sensor network. It is vital to detect community structure in complex networks and to take advantage of extracted information. On the basis, community detection has become a popular theme in the field of complex networks. It focuses on uncovering the affiliation of each node through topology analysis. In this chapter, we have presented the essential knowledge and advanced methods of community detection, including proximate support vector clustering (PSVC), deep auto-encoded extreme learning machine (DA-ELM), deep auto-coded clustering (DAC) and local aggregated differential evolution algorithm (LADE). These methods have been applied in tree-based WSNs and have been proved in terms of robustness and fragility.