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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44248

Novel Compression Based Community Detection Approach Using Hybrid Honey Badger Bonobo Optimizer for Online Social Networks

K. Sankara Nayaki , M. Sudheep Elayidom , R. Rajesh


Online social media community detection identifies node connections. Clusters, modules, or groups in different networks define the community. Community detection finds hidden network relationships. Several works have been done to detect network node communities, but performance is often affected by imprecise detection, time complexity, etc. We proposed a Hybrid Honey Badger Bonobo Optimization to detect network node communities (HHBBO)[34]. Before applying HHBBO, networks are compressed to reduce time complexity and identify node communities. Global optimization can be achieved using HBO and BO. Hybrid algorithms optimize global search. This searches nodes globally and detects their relationships. Experimental analyses show that the proposed approach can detect online social media node communities more effectively than other approaches. GA, LSMD, DPCD, ICLA and HHBAVO are used for comparison.


Community Detection; Badger; network; clusters; compressed network; and hybrid

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