An efficient and principled method for detecting communities in networks
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Summary
This work describes a method for finding overlapping communities based on a principled statistical approach using generative network models and shows how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times.
- Type
- article
- Published
- 2011-04-18
- Cited by
- 398
- References
- 59
- Access
- Open access
- OpenAlex
- https://openalex.org/W1972675431
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14204351
Keywords
Computer science, Disjoint sets, Maximization, Relaxation (psychology), Community structure
References
- Learning the parts of objects by non-negative matrix factorization
- Community structure in social and biological networks
- Modularity optimization in community detection of complex networks
- Stochastic Blockmodels for Directed Graphs
- Evaluating local community methods in networks
- The dynamics of viral marketing
- Community detection algorithms: a comparative analysis: invited presentation, extended abstract
- Finding community structure in networks using the eigenvectors of matrices.
- Benchmark graphs for testing community detection algorithms.
- An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling
- Line graphs, link partitions, and overlapping communities.
- A Critical Point for Random Graphs with a Given Degree Sequence
- Latent semantic models for collaborative filtering
- Overlapping community detection using Bayesian non-negative matrix factorization.
- Performance of modularity maximization in practical contexts.
- On an equivalence between PLSI and LDA
- A nonparametric view of network models and Newman–Girvan and other modularities
- An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks
- Finding and evaluating community structure in networks.
- Stochastic blockmodels: First steps
Cited by
- Publishing Practices and the Role of Publication in the Work of Academics in the Mathematics Education Research Community in England
- Relationship discovery in social networks. (Découverte des relations dans les réseaux sociaux)
- Cluster damage robustness analysis and space independent community detection in complex networks
- Why Steiner-tree type algorithms work for community detection
- Bayesian Nonparametric Poisson Factorization for Recommendation Systems
- Developing Profiles of Malware and User Behaviors Using Graph-Mining and Machine Learning Techniques
- A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks
- An Adaptive Spectral Algorithm for the Recovery of Overlapping Communities in Networks
- Inferring the mesoscale structure of layered, edge-valued, and time-varying networks.
- Modeling with Node Degree Preservation Can Accurately Find Communities
- Overlapping Communities Detection via Measure Space Embedding
- An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory
- Overlapping Community Detection by Online Cluster Aggregation
- Likelihood-based model selection for stochastic block models
- Overlapping Community Detection Algorithm Based on the Law of Universal Gravitation
- STATISTICS OF DYNAMIC RANDOM NETWORKS: A DEPTH FUNCTION APPROACH.
- Adapting the Stochastic Block Model to Edge-Weighted Networks
- Efficient detection of communities with significant overlaps in networks: Partial community merger algorithm
- Community Detection in Networks using Graph Distance
- Community detection for proximity alignment
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