Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks

Community detection aims to partition a set of nodes with more similarities in the set than out of it based on different criteria like neighborhood similarity or vertex connectivity. Most present day community detection methods principally concentrate on the topological structure, largely ignoring the heterogeneous properties of the vertex. This paper proposes a new community detection model, based on the possibilistic c-means model, by using structural as well as attribute similarities in a large scale in social networks. In the majority of real social networks, different clusters share nodes, resulting in the formation of overlapping communities. The proposed model, on the basis of structural and attribute similarity (PCMSA), serves as a fuzzy community detection model addressing the overlapping community detection problem, and detecting communities in a way that each community has a densely connected sub-graph with homogeneous attribute values. The function of the proposed model is assessed by a trade-off between intra-cluster and inter-cluster density and homogeneity. Therefore, to validate the proposed community detection algorithm (PCMSA) and its results, an index, compatible with the proposed model, is defined; and to assess the efficiency of the proposed fuzzy community detection, several experimental results in variety sizes from very small to very large sizes of real social networks are given, and the results are contrasted with other community detection models like FCAN, CODICIL, SA-cluster, K-SNAP and PCM. The experimental findings reveal the superiority of this novel model and its promising scalability and computational complexity over others.