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
Effective Hierarchical Information Threading Using Network Community Detection
With the tremendous growth in the volume of information produced online every day (e. g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i. e., information threads).
Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
In this paper, we introduce a novel and computationally efficient method for vertex embedding, community detection, and community size determination.
Artificial Benchmark for Community Detection with Outliers (ABCD+o)
The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes.
Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection
Motivated by this discovery, this paper proposes a novel Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network’s symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network’s intrinsic geometry into its learning objective, thus making the achieved detector well-aware of community structure within the target network.
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
guillaumesalhagalvan/modularity_aware_gae
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It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features.
MGTCOM: Community Detection in Multimodal Graphs
egordm/mgtcom
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Importantly, MGTCOM is an end-to-end framework optimizing network embeddings, communities, and the number of communities in tandem.
A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
nhuang37/gnn_community_detection
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In this work we propose a random graph model that can produce graphs at different levels of sparsity.
Hypergraph Artificial Benchmark for Community Detection (h-ABCD)
The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes.
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation
Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs.
Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis
The data reflect a significant degree of discursive dissimilarity between known Russian disinformation bots and a control set of Twitter users during the timeframe of the 2016 U. S. Presidential Election.
