FuzAg: Fuzzy Agglomerative Community Detection by Exploring the Notion of Self-Membership

In this paper, a fuzzy agglomerative (FuzAg) approach is proposed for community detection that iteratively updates membership degree of nodes. Earlier approaches assign membership degree to nodes based on communities only. We introduce the notion of
self-membership
in addition to the membership of different communities. The essence of self-membership is to give opportunity to all nodes in growing their own community. Nodes having higher self-membership degree are referred as
anchors
, and they get a chance to expand their associated community. Meanwhile, some new anchors may emerge in successive iterations, whereas
false or redundant anchors
get removed. The time complexity of the proposed algorithm is shown to be
$O(n^2)$
. We compare the results of the proposed FuzAg algorithm with those of state-of-the-art fuzzy community detection algorithms on ten real-world datasets as well as on synthetic networks. Results indicated by various quality and accuracy metrics show impressive performance of FuzAg in identifying both disjoint communities and fuzzy communities.