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
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Social networks are often associated with rich side information, such as texts and images.
Learning dynamic representations of the functional connectome in neurobiological networks
We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times.
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Wildfire prediction has become increasingly crucial due to the escalating impacts of climate change.
Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis
Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution.
A Structural-Clustering Based Active Learning for Graph Neural Networks
To address this problem, we propose the Structural-Clustering PageRank method for improved Active learning (SPA) specifically designed for graph-structured data.
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
GraphResearcher/HyGCL-DC
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2023 IEEE International Conference on Data Mining (ICDM) 2023
To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities.
Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters
To this end, we introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data, which seamlessly integrates information from different views without the need for a predefined number of clusters.
Hypergraphs with node attributes: structure and inference
Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace.
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
6lyc/cdnmf
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Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.
Explaining Interactions Between Text Spans
copenlu/spanex
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Reasoning over spans of tokens from different parts of the input is essential for natural language understanding (NLU) tasks such as fact-checking (FC), machine reading comprehension (MRC) or natural language inference (NLI).
