Community Detection
227 papers with code • 14 benchmarks • 12 datasets
Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.
Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Benchmarks
These leaderboards are used to track progress in Community Detection
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Libraries
Use these libraries to find Community Detection models and implementations
Pubmed
Cora
SNAP
Placenta
Latest papers with no code
Graph Vertex Embeddings: Distance, Regularization and Community Detection
Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space, enabling the use of efficient methods that employ the metric structure in the embedding space as a proxy for the topological structure of the data.
Estimating mixed memberships in multi-layer networks
For real-world multi-layer networks with unknown community information, we introduce two novel modularity metrics to quantify the quality of mixed membership community detection.
Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System
The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity.
Community detection by spectral methods in multi-layer networks
Moreover, our analysis indicates that spectral clustering with the debiased sum of squared adjacency matrices is generally superior to spectral clustering with the sum of adjacency matrices.
Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks.
Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach
To the author's knowledge, this method has not been employed in the Persian language before on Instagram social network.
An Effective Index for Truss-based Community Search on Large Directed Graphs
Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks.
Community Detection in the Multi-View Stochastic Block Model
This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective.
Fundamental limits of community detection from multi-view data: multi-layer, dynamic and partially labeled block models
Multi-view data arises frequently in modern network analysis e. g. relations of multiple types among individuals in social network analysis, longitudinal measurements of interactions among observational units, annotated networks with noisy partial labeling of vertices etc.
Mixture of multilayer stochastic block models for multiview clustering
In this work, we propose an original method for aggregating multiple clustering coming from different sources of information.
