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Community Detection

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

Papers

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Showing 1–10 of 919 papers

TitleStatusHype
Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs—0
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning ApproachCode0
Identifying Hubs Through Influential Nodes in Transportation Network by Using a Gravity Centrality ApproachCode0
Strongly Consistent Community Detection in Popularity Adjusted Block Models—0
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation AlgorithmCode1
Optimal community detection in dense bipartite graphs—0
Pre-trained Prompt-driven Semi-supervised Local Community Detection—0
Breaking the Batch Barrier (B3) of Contrastive Learning via Smart Batch MiningCode1
Advancing Community Detection with Graph Convolutional Neural Networks: Bridging Topological and Attributive CohesionCode0
NAZM: Network Analysis of Zonal Metrics in Persian Poetic Tradition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.71—Unverified