A pseudo-likelihood approach to community detection in weighted networks
Not on the map. Why?
Abstract (arXiv)
Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns. While many community detection methods have been developed for networks with binary edges, few of them are applicable to networks with weighted edges, which are common in practice. We propose a pseudo-likelihood community estimation algorithm derived under the weighted stochastic block model for networks with normally distributed edge weights, extending the pseudo-likelihood algorithm for binary networks, which offers some of the best combinations of accuracy and computational efficiency. We prove that the estimates obtained by the proposed method are consistent under the assumption of homogeneous networks, a weighted analogue of the planted partition model, and show that they work well in practice for both homogeneous and heterogeneous networks. We illustrate the method on simulated networks and on a fMRI dataset, where edge weights represent connectivity between brain regions and are expected to be close to normal in distribution by construction.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 10 Mar 2023.
Builds on
15 of 34 references.
References · 15
- 2017Community Detection and Stochastic Block Modelscited by 319
- 2017Cooperative Game Theory Approaches for Network Partitioningcited by 135
- 2013Consistency of spectral clustering in stochastic block modelscited by 129
- 2019Inference for multiple heterogeneous networks with a common invariant subspacecited by 39
- 2014Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatricescited by 29
- 2014Learning Latent Block Structure in Weighted Networkscited by 23
- 2014Detecting Overlapping Communities in Networks Using Spectral Methodscited by 22
- 2020Latent space models for multiplex networks with shared structurecited by 14
- 2017Network classification with applications to brain connectomicscited by 12
- 2017Joint Embedding of Graphscited by 12
- 2018Spectral Clustering for Multiple Sparse Networks: Icited by 8
- 2019Recovering shared structure from multiple networks with unknown edge distributionscited by 8
3 more
- 2017Estimating a network from multiple noisy realizationscited by 7
- 2015Submatrix localization via message passingcited by 4
- 2019Graph-aware Modeling of Brain Connectivity Networkscited by 1
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.