Fuzzy communities and the concept of bridgeness in complex networks

TL;DR

This paper introduces a fuzzy community detection method in networks that assigns membership degrees to vertices, identifies outliers and bridges, and can predict community structure under uncertainty, applicable to various real-world networks.

Contribution

It presents a novel algorithm for fuzzy community detection with a new measure for identifying outliers, bridges, and central vertices, enhancing understanding of complex network structures.

Findings

Successfully applied to social, collaboration, and cortical networks

Identifies outliers, bridges, and core vertices effectively

Provides a method for community prediction under data uncertainty

Abstract

We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We created an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a new measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that belong significantly to more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the dataset analyzed. The number of communities can be given in advance, or determined by the algorithm itself using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to social networks, scientific collaboration networks and cortical networks with high confidence.