A Generalized Modularity for Computing Community Structure in Fully Signed Networks

Abstract

The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a generalized framework of community structure in fully signed networks, based on which a variant of modularity is designed. An optimization algorithm that maximizes modularity to detect potential communities is also proposed. Experiments show that the proposed method can efficiently optimize the objective function and perform effective community detection.

A Review of Community Detection Based on Modularity Optimization in Complex Networks

“…It is worth noting that many efforts were made to deal with such a problem, for instance, by building a quality function starting with the quality expression of a single community [29] and gaining the best partition in a confirmed number of clusters. Although, for the general case, using optimization of quality functions for identifying communities is still adopted [30][31][32].…”

“…strong consistency with a growing K (number of communities) [79], [82] strong consistency: sharp threshold under sparse networks [83], [84], [85] weak consistency [86], [87] non-trivial recovery [88], [89], [90] robustness against outlier nodes [79], [91], [92] consistency under degree-corrected models [31] consistency under weak assortativity [93], [94] 6. Experimental part Many different algorithms have been proposed for network community detection, as mentioned in previous sections.…”