GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

Local overlapping community detection is a hot problem in the field of studying complex networks. It process finding dense clusters based on local network information. This paper proposes method called greedy extended dynamic (GLOD) to address challenges detecting high-quality communities The goal improve accuracy by considering nature boundaries and leveraging GLOD consists several steps. First, coupling seed constructed selecting nodes from blank (i.e., not assigned any community) their similar neighboring nodes. serves as starting point for detection. Next, are applying multiple fitness functions. These functions determine likelihood belonging specific various properties. By iteratively expanding boundaries, with higher density better internal structure formed. Finally, merged using an improved version Jaccard coefficient, which measure similarity between sets. step ensures that properly identified accounted final structure. proposed evaluated real networks three sets LFR (Lancichinetti–Fortunato–Radicchi) networks, synthetic benchmark widely used research. experimental results demonstrate outperforms existing algorithms achieves 2.1% improvement F-score, quality evaluation metric, compared LOCD framework. best algorithm provenance network. In summary, aims overcome limitations incorporating information, communities, dynamically adjusting boundaries. suggest effective improving