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The capacity to identify and analyze protein-protein interactions, along withtheir internal modular organization, plays a crucial role in comprehending theintricate mechanisms underlying biological processes at the molecular level. Wecan learn a lot about the structure and dynamics of these interactions by usingnetwork analysis. We can improve our understanding of the biological roots ofdisease pathogenesis by recognizing network communities. This knowledge, inturn, holds significant potential for driving advancements in drug discoveryand facilitating personalized medicine approaches for disease treatment. Inthis study, we aimed to uncover the communities within the variant B.1.1.529(Omicron virus) using two proposed novel algorithm (ABCDE and ALCDE) and fourwidely recognized algorithms: Girvan-Newman, Louvain, Leiden, and LabelPropagation algorithm. Each of these algorithms has established prominence inthe field and offers unique perspectives on identifying communities withincomplex networks. We also compare the networks by the global properties,statistic summary, subgraph count, graphlet and validate by the modulaity. Byemploying these approaches, we sought to gain deeper insights into thestructural organization and interconnections present within the Omicron virusnetwork.

Two Novel Approaches to Detect Community: A Case Study of Omicron Lineage Variants PPI Network