Community detection with greedy modularity disassembly strategy

MABBI – Research conducted by Heru Cahya Rustamaji, Wisnu Ananta Kusuma, Sri Nurdiati, and Irmanida Batubara from IPB University entitled Community detection with greedy modularity disassembly strategy
One of the problems in networking is community detection, the process of identifying groups of nodes in a network that are more densely connected than the rest of the network. To measure the quality of community detection in the network using modularity. Greedy modularity is one of the community detection algorithms. It optimizes the modularity score locally at each step rather than globally optimizing over all possible network partitions. However, because the algorithm makes local improvements, it can be trapped in a suboptimal network partition. In this research, we developed the Greedy Modularity algorithm by adding an exploration method: a disassembly node and disassembly community formed to increase modularity. There are four strategies for disassembly nodes: disassembly random nodes, weak nodes, nodes with low embeddedness values, and releasing nodes that do not form triads. The community disassembly strategy includes random communities, weak communities, communities with low internal edge density, communities with low triad participation ratio, and communities with low conductance. The results show increased modularity in various real-world and synthetic network datasets. We also compared the results with various community detection algorithms, and the modularity ranking of these algorithms were in positions 1-8 across various datasets. (Tri/MABBI)

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