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 110 of 393 papers

TitleStatusHype
Tri-Learn Graph Fusion Network for Attributed Graph ClusteringCode1
Unsupervised Graph Clustering with Deep Structural EntropyCode0
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning0
Rhomboid Tiling for Geometric Graph Deep Learning0
A Geometric Approach to Problems in Optimization and Data Science0
HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering0
Adaptive Local Clustering over Attributed GraphsCode0
Riemannian Optimization on Relaxed Indicator Matrix ManifoldCode2
PanoGS: Gaussian-based Panoptic Segmentation for 3D Open Vocabulary Scene Understanding0
Deep Cut-informed Graph Embedding and Clustering0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1R-DGAEACC70.5Unverified
2R-GMM-VGAEACC68.9Unverified
3AGCACC67Unverified
4RWR-GAEACC61.6Unverified
5RWR-VGAEACC61.3Unverified
6ARGEACC57.3Unverified
7ARVGEACC54.4Unverified
8GAEACC40.8Unverified
9DAEGC+GSCAN†NMI39.9Unverified