Highly Connected Subgraph (HCS) Clustering: Definition, Algorithm, and Applications

Highly Connected Subgraph (HCS) Clustering is a graph-based clustering algorithm designed to identify highly connected subgraphs (HCS) within a network. An HCS is a subgraph composed of a group of nodes that are densely interconnected, with a connection density significantly higher than the average connection density of the entire graph. The goal of HCS Clustering is to partition the nodes of a graph into distinct HCS, enabling a deeper understanding and analysis of the graph's structure and characteristics.

The core principle of HCS Clustering involves identifying HCS by calculating node similarity and connection density. Initially, the algorithm calculates a similarity matrix to determine the similarity between nodes. Subsequently, using the similarity matrix, it computes the connection density between nodes, ultimately identifying HCS. Finally, the algorithm assigns unique identifiers to each HCS and partitions the nodes into these identified groups.

HCS Clustering finds wide application in various fields, including social network analysis, bioinformatics, and network security. Its effectiveness as a clustering algorithm empowers users to gain a more comprehensive understanding and analyze the structure and characteristics of graphs.