SOTAVerified

Graph Clustering

Graph Clustering is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.

Source: Clustering for Graph Datasets via Gumbel Softmax

Papers

Showing 351–375 of 393 papers

TitleStatusHype
Graphons, mergeons, and so on!—0
Information Recovery in Shuffled Graphs via Graph Matching—0
Do logarithmic proximity measures outperform plain ones in graph clustering?—0
Graph Clustering Bandits for Recommendation—0
Phase Transitions and a Model Order Selection Criterion for Spectral Graph ClusteringCode0
Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images—0
Deep neural networks for learning graph representations—0
Spectral Theory of Unsigned and Signed Graphs. Applications to Graph Clustering: a SurveyCode0
Multicuts and Perturb & MAP for Probabilistic Graph Clustering—0
An Empirical Comparison of the Summarization Power of Graph Clustering Methods—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
Integration of graph clustering with ant colony optimization for feature selection—0
Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the FOMC Meeting Transcriptions—0
Opinion Holder and Target Extraction based on the Induction of Verbal Categories—0
Graphs in machine learning: an introduction—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
Median evidential c-means algorithm and its application to community detection—0
Clustering from Labels and Time-Varying Graphs—0
Graph Clustering With Missing Data: Convex Algorithms and Analysis—0
An application of topological graph clustering to protein function prediction—0
A Genetic Algorithm for Software Design Migration from Structured to Object Oriented Paradigm—0
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering—0
Distributed Distributional Similarities of Google Books Over the Centuries—0
Parallel Graph Partitioning for Complex NetworksCode0
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1R-DGAEACC70.5—Unverified
2R-GMM-VGAEACC68.9—Unverified
3AGCACC67—Unverified
4RWR-GAEACC61.6—Unverified
5RWR-VGAEACC61.3—Unverified
6ARGEACC57.3—Unverified
7ARVGEACC54.4—Unverified
8GAEACC40.8—Unverified
9DAEGC+GSCAN†NMI39.9—Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC76.7—Unverified
2R-DGAEACC73.7—Unverified
3AGCACC68.92—Unverified
4RWR-VGAEACC68.5—Unverified
5RWR-GAEACC66.9—Unverified
6ARGEACC64—Unverified
7ARVGEACC63.8—Unverified
8GAEACC59.6—Unverified
9DAEGC+GSCAN†NMI52.4—Unverified
#ModelMetricClaimedVerifiedStatus
1R-GMM-VGAEACC74—Unverified
2RWR-VGAEACC73.6—Unverified
3RWR-GAEACC72.6—Unverified
4R-DGAEACC71.4—Unverified
5AGCACC69.78—Unverified
6VGAEACC65.48—Unverified
7DAEGC+GSCAN†NMI31.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index1—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.46—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1Polaratio Consensus ClusteringAdjusted Rand Index0.81—Unverified