ArticleProceedings of the National Academy of Sciences of the United States of America2013
Efficient discovery of overlapping communities in massive networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Benchmarking overlapping community detection methods for applications in human connectomics.Network neuroscience (Cambridge, Mass.) · 2026Article
- Learning dynamic representations of the functional connectome in neurobiological networks.ArXiv · 2024Article
- Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization.Nature communications · 2024Article
- Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold.Journal of machine learning · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
- Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization.Research square · 2023Article
- Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition.Human brain mapping · 2023Article
- Estimating Mixed Memberships in Directed Networks by Spectral Clustering.Entropy (Basel, Switzerland) · 2023Article
- LazyFox: fast and parallelized overlapping community detection in large graphs.PeerJ. Computer science · 2023Article
- A population-based study of precision health assessments using multi-omics network-derived biological functional modules.Cell reports. Medicine · 2022Article
- Isolate sets partition benefits community detection of parallel Louvain method.Scientific reports · 2022Article
- Two provably consistent divide-and-conquer clustering algorithms for large networks.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture.Nature neuroscience · 2020Article
- Envisioning Insight-Driven Learning Based on Thick Data Analytics With Focus on Healthcare.IEEE access : practical innovations, open solutions · 2020Article
- Element-centric clustering comparison unifies overlaps and hierarchy.Scientific reports · 2019Article
- Mapping the spectrum of 3D communities in human chromosome conformation capture data.Scientific reports · 2019Article
- Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model.Molecular biology and evolution · 2019Article
- Dissecting the Shared and Context-Dependent Pathways Mediated by the p140Cap Adaptor Protein in Cancer and in Neurons.Frontiers in cell and developmental biology · 2019Article
- Gauss's law for networks directly reveals community boundaries.Scientific reports · 2018Article
- Prioritizing network communities.Nature communications · 2018Article
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2 authors.
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Abstract
Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.