Finding and evaluating community structure in networks

Finding and evaluating community structure in networks

11 Aug 2003 | M. E. J. Newman1,2 and M. Girvan2,3
The paper by Newman and Girvan introduces a set of algorithms for discovering community structure in networks, which involves iteratively removing edges to split the network into communities. The algorithms use "betweenness" measures to identify edges that connect different communities, and these measures are recalculated after each edge removal. The authors propose a measure called "modularity" to quantify the strength of the community structure found by the algorithms, providing an objective metric for determining the number of communities. The algorithms are effective in both computer-generated and real-world network data, and they can help reveal the complex structure of networked systems. The paper discusses the implementation details, including the calculation of betweenness scores for edges, and provides examples of their application to various networks, such as Zachary's karate club network, a collaboration network of scientists, and a network of dolphins. The results demonstrate the algorithms' ability to accurately identify community structures and their usefulness in understanding networked systems.The paper by Newman and Girvan introduces a set of algorithms for discovering community structure in networks, which involves iteratively removing edges to split the network into communities. The algorithms use "betweenness" measures to identify edges that connect different communities, and these measures are recalculated after each edge removal. The authors propose a measure called "modularity" to quantify the strength of the community structure found by the algorithms, providing an objective metric for determining the number of communities. The algorithms are effective in both computer-generated and real-world network data, and they can help reveal the complex structure of networked systems. The paper discusses the implementation details, including the calculation of betweenness scores for edges, and provides examples of their application to various networks, such as Zachary's karate club network, a collaboration network of scientists, and a network of dolphins. The results demonstrate the algorithms' ability to accurately identify community structures and their usefulness in understanding networked systems.
Reach us at info@study.space