Weighted Community Fusion for Community Detection and Relationship Modelling In Complex Networks

Abstract

Community detection is an important step in perceiving network structure and performance for complex network
analysis. The rapid growth of network data in recent years has piqued the interest of many researchers in
community detection. The majority of community detection methods only consider the network structure.
Nonetheless, real-world network nodes may have some characteristics that can be useful for community
detection. Most existing optimization-based community detection algorithms are only applicable to disjoint
community structure. However, it has been shown that in most real-world networks, a node may belong to
multiple communities implying overlapping community structure. Combining multiple community relationship
networks (CRNs) is crucial for understanding the holistic interactions among individuals or groups across
different platforms or contexts. Existing methods often fail to preserve community structures or handle
conflicting edge information effectively. This paper proposes a novel algorithm, Weighted Community Fusion
(WCF), which aligns nodes, fuses edges using weighted aggregation, preserves overlapping communities, and
iteratively refines the network structure. Experimental results on synthetic and real-world datasets demonstrate
that WCF outperforms traditional fusion methods in maintaining community coherence and network connectivity.