Representative community divisions of networks
arXiv:2105.04612 · doi:10.1038/s42005-022-00816-3
Abstract
Methods for detecting community structure in networks typically aim to identify a single best partition of network nodes into communities, often by optimizing some objective function, but in real-world applications there may be many competitive partitions with objective scores close to the global optimum and one can obtain a more informative picture of the community structure by examining a representative set of such high-scoring partitions than by looking at just the single optimum. However, such a set can be difficult to interpret since its size can easily run to hundreds or thousands of partitions. In this paper we present a method for analyzing large partition sets by dividing them into groups of similar partitions and then identifying an archetypal partition as a representative of each group. The resulting set of archetypal partitions provides a succinct, interpretable summary of the form and variety of community structure in any network. We demonstrate the method on a range of example networks.
arXiv comment15 pages, 4 figures
References in corpus (24)
- Community structure in social and biological networks
- Modularity and community structure in networks
- Community detection in graphs
- Fast algorithm for detecting community structure in networks
- Finding community structure in networks using the eigenvectors of matrices
- Maps of random walks on complex networks reveal community structure
- Resolution limit in community detection
- Statistical Mechanics of Community Detection
- Stochastic blockmodels and community structure in networks
- The performance of modularity maximization in practical contexts
- Modularity from Fluctuations in Random Graphs and Complex Networks
- Consensus clustering in complex networks
- An information-theoretic framework for resolving community structure in complex networks
- Missing and spurious interactions and the reconstruction of complex networks
- Nonparametric Bayesian inference of the microcanonical stochastic block model
- Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
- Identifying "communities" within energy landscapes
- Bayesian stochastic blockmodeling
- Cross-Entropy Clustering
- Improved mutual information measure for classification and community detection
- Consistency of community structure in complex networks
- Exploring the solution landscape enables more reliable network community detection
- Merge-split Markov chain Monte Carlo for community detection
- Information theoretic network approach to socioeconomic correlations