Community Detection in Diverse Networks to Locate Influential Spreaders based on K-core and Random Walk Algorithms

Community Detection in Social Network plays a crucial role in understanding the underlying structure and dynamics of complex systems. To get insights into network dynamics and connectivity patterns, this work aims to identify communities within the datasets and analyse their structural characteristics in order to spread the information from the influential one. Therefore, this study uses K-core and Random Walk algorithms to extract comprehend communities and clusters from the network and to compare how well community discovery performed across two different datasets. The datasets included in the analysis are the Gnutella peer-to-peer network made up of snapshots from August 2002 and the Wikipedia vote network, which records voting relationships among users. Both algorithms were implemented using NetworkX package and each dataset’s study reveals various communities. While the Gnutella peer-to-peer network displays file sharing clusters, the Wikipedia vote network analysis offers insights into voting trends. The result shows how well the used algorithms function at revealing community structures in various network datasets.