Community Detection

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122 papers with code • 12 benchmarks • 7 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

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Trend	Dataset	Best Model	Results
	Amazon	CommunityGAN CommunityGAN: Community Detection with Generative Adversarial Nets	3
	Facebook Politicians	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook TV Show	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Companies	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Athletes	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Media	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Celebrities	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Artists	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Facebook Government	Smooth GEMSEC 2 GEMSEC: Graph Embedding with Self Clustering	1
	Cora	EdMot EdMot: An Edge Enhancement Approach for Motif-aware Community Detection	1
	2010 i2b2/VA	brian Community detection with spiking neural networks for neuromorphic hardware	1
	DBLP	CommunityGAN CommunityGAN: Community Detection with Generative Adversarial Nets	1

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Paper	Code	Results	Date

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