Graph Clustering
145 papers with code • 10 benchmarks • 18 datasets
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.
Benchmarks
These leaderboards are used to track progress in Graph Clustering
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Libraries
Use these libraries to find Graph Clustering models and implementationsDatasets
Pubmed
Cora
SNAP
Netzschleuder
97 synthetic datasets
Placenta
Most implemented papers
Attention-driven Graph Clustering Network
The combination of the traditional convolutional network (i. e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Deep Graph Clustering via Dual Correlation Reduction
yueliu1999/awesome-deep-graph-clustering
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To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.
Attributed Graph Clustering with Dual Redundancy Reduction
gongleii/AGC-DRR
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Conference 2022
To this end, we develop a novel method termed attributed graph clustering with dual redundancy reduction (AGC-DRR) to reduce the information redundancy in both input space and latent feature space.
NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering
However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated; 2) depend too much on data augmentation, which greatly limits the capability of contrastive learning; 3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results.
RuDSI: graph-based word sense induction dataset for Russian
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
yueliu1999/awesome-deep-graph-clustering
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However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
Hard Sample Aware Network for Contrastive Deep Graph Clustering
yueliu1999/awesome-deep-graph-clustering
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Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
Reinforcement Graph Clustering with Unknown Cluster Number
yueliu1999/awesome-deep-graph-clustering
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To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC).
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
