communitydeteksjon

Community detection is a task in network analysis that aims to identify groups of nodes that are more densely connected among themselves than with the rest of the network. These groups, often referred to as communities, clusters, or modules, represent cohesive structures within a larger complex system. The fundamental idea is that nodes within a community share similar properties or functions, and their connections are more frequent and stronger compared to connections with nodes outside their community.

Various algorithms exist for community detection, each employing different approaches to measure and identify these dense subgraphs. Some common methods include modularity optimization, which seeks to partition the network in a way that maximizes the difference between the number of edges within communities and the number of edges expected by chance. Other approaches utilize hierarchical clustering, density-based clustering, or spectral methods. The choice of algorithm often depends on the specific characteristics of the network and the desired properties of the detected communities.

The applications of community detection are widespread across many fields. In social networks, it can reveal social groups or interest-based communities. In biological networks, it can identify functional modules of genes or proteins. In the internet, it might uncover clusters of related websites or communities of users. Understanding these community structures can provide valuable insights into the organization, behavior, and dynamics of complex systems.