Modularitätsanalysen

Modularitätsanalysen, also known as modularity analysis, is a method used in network theory to detect communities, groups, or clusters within a network. It was introduced by Mark Newman in 2004 as a way to measure the strength of division of a network into modules or communities. The primary goal of modularity analysis is to identify whether a network can be divided into distinct groups of nodes that are more densely connected internally than they are with nodes outside the group.

The modularity Q of a network is a scalar value between -1 and 1 that measures the degree to which the network can be divided into such modules. A high value of Q indicates strong community structure, while a low or negative value indicates that the network is not well divided into modules. The modularity is calculated based on the difference between the actual number of edges within communities and the expected number of such edges in a randomized network with the same degree distribution.

Modularity analysis is widely used in various fields, including social network analysis, biology, and computer science. It helps in understanding the structure and function of complex networks, identifying key nodes or communities, and predicting the behavior of networked systems. Several algorithms and tools are available for performing modularity analysis, such as the Louvain method, Girvan-Newman algorithm, and Infomap.

In summary, modularity analysis is a powerful technique for uncovering the underlying community structure of complex networks. It provides valuable insights into the organization and dynamics of networked systems, making it an essential tool in network science and related disciplines.