A novel compression based community detection approach using hybrid honey badger African vulture optimization for online social networks

Community detection in online social media networks is to identify the connections of nodes within the network. The community can be determined as clusters, modules, or groups in different networks. Community detection is performed to find out the hidden relationships among the nodes in the network. Several works have been conducted till now to detect the community of nodes in the network however the performance is often affected due to the imprecise detection, time complexity, and so on. To detect the community of the nodes in the network effectively we have proposed a novel hybrid honey badger optimization‐based African vulture algorithm (HHBAVO). Prior to the application of HHBAVO, the networks are compressed to reduce the time complexity and effective identification of the community of nodes. The proposed honey badger optimization (HBO) and African vulture optimization (AVO) can be used to achieve global optimization. The algorithms are mainly hybridized to offer optimized global search. This is effectively used to search the nodes globally and to detect the relationship among the nodes. Experimental analyzes depict that the proposed approach can be used to detect the community of the nodes in the online social media networks effectively than the other approaches. For comparative purposes, we have taken state‐of‐art works such as GA, LSMD, DPCD, and ICLA approaches.