Közösségdetektálás

Közösségdetektálás is a process in network analysis that aims to identify groups of nodes within a larger network that are more densely connected to each other than to the rest of the network. These densely connected groups are often referred to as communities, clusters, or modules. The fundamental idea is that real-world networks, such as social networks, biological networks, or the internet, are not uniformly random but exhibit a modular structure. Identifying these communities can reveal underlying organizational principles, functional roles, or relationships within the network.

There are various algorithms and approaches to community detection, differing in their underlying principles and suitability for different network types. Some common methods include partition-based algorithms, which aim to divide the network into a set number of communities, and hierarchical algorithms, which build a hierarchy of communities. Modularity maximization is a popular objective function used in many algorithms, aiming to find a partition that maximizes the difference between the number of edges within communities and the number of edges expected in a random network with the same degree distribution. Other approaches include label propagation, spectral clustering, and flow-based methods.

The applications of community detection are widespread. In social network analysis, it can identify groups of friends, colleagues, or individuals with shared interests. In biology, it can reveal functional modules in protein-protein interaction networks or gene regulatory networks. On the internet, it can help in understanding the structure of the World Wide Web or identifying communities of websites. The effectiveness of a community detection algorithm often depends on the specific characteristics of the network and the definition of what constitutes a community in that context.