An algorithm for network community structure determination by surprise
arXiv:2012.13780 · doi:10.1016/j.physa.2022.127063
Abstract
Graphs representing real world systems may be studied from their underlying community structure. A community in a network is an intuitive idea for which there is no consensus on its objective mathematical definition. The most used metric in order to detect communities is the modularity, though many disadvantages of this parameter have already been noticed in the literature. In this work, we present a new approach based on a different metric: the surprise. Moreover, the biases of different community detection algorithms and benchmark networks are thoroughly studied, identified and commented about.
arXiv comment29 pages, 6 figures, 7 tables
References in corpus (19)
- Fast unfolding of communities in large networks
- Statistical mechanics of complex networks
- Modularity and community structure in networks
- From Louvain to Leiden: guaranteeing well-connected communities
- Maps of random walks on complex networks reveal community structure
- Benchmark graphs for testing community detection algorithms
- Resolution limit in community detection
- Statistical Mechanics of Community Detection
- Structural Vulnerability of the North American Power Grid
- The performance of modularity maximization in practical contexts
- Narrow scope for resolution-limit-free community detection
- Local resolution-limit-free Potts model for community detection
- Deciphering Network Community Structure by Surprise
- Surprise maximization reveals the community structure of complex networks
- Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering
- A comprehensive statistical study of metabolic and protein-protein interaction network properties
- SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization
- Closed benchmarks for network community structure characterization
- Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction