Two Novel Approaches to Detect Community: A Case Study of Omicron
  Lineage Variants PPI Network
Mamata Das Selvakumar K. P. J. A. Alphonse
The capacity to identify and analyze protein-protein interactions, along with
their internal modular organization, plays a crucial role in comprehending the
intricate mechanisms underlying biological processes at the molecular level. We
can learn a lot about the structure and dynamics of these interactions by using
network analysis. We can improve our understanding of the biological roots of
disease pathogenesis by recognizing network communities. This knowledge, in
turn, holds significant potential for driving advancements in drug discovery
and facilitating personalized medicine approaches for disease treatment. In
this 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 four
widely recognized algorithms: Girvan-Newman, Louvain, Leiden, and Label
Propagation algorithm. Each of these algorithms has established prominence in
the field and offers unique perspectives on identifying communities within
complex networks. We also compare the networks by the global properties,
statistic summary, subgraph count, graphlet and validate by the modulaity. By
employing these approaches, we sought to gain deeper insights into the
structural organization and interconnections present within the Omicron virus
network.