Graph Clustering

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

19 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 180 papers with code (393 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Variational Graph Auto-Encoders
  21 Nov 2016 22 repositories listed Syntology ran 7 of 13 samples · 6 unverified
  We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
  20 May 2019 6 repositories listed
  Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.
• Spectral Clustering with Graph Neural Networks for Graph Pooling
  30 Jun 2019 4 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.
• Adversarially Regularized Graph Autoencoder for Graph Embedding
  13 Feb 2018 4 repositories listed
  Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
• Attributed Graph Clustering: A Deep Attentional Embedding Approach
  15 Jun 2019 3 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 1 pointer-only (licence)
  Graph clustering is a fundamental task which discovers communities or groups in networks.
• Ensemble Clustering for Graphs
  14 Sep 2018 3 repositories listed
  We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.
• Hierarchical Graph Clustering using Node Pair Sampling
  5 Jun 2018 3 repositories listed
  We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques.
• Synergistic Deep Graph Clustering Network
  22 Jun 2024 2 repositories listed
  Employing graph neural networks (GNNs) to learn cohesive and discriminative node representations for clustering has shown promising results in deep graph clustering.
• MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion
  7 Dec 2023 2 repositories listed
  As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
• CONVERT:Contrastive Graph Clustering with Reliable Augmentation
  17 Aug 2023 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 7 pointer-only (licence)
  To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
• Reinforcement Graph Clustering with Unknown Cluster Number
  13 Aug 2023 2 repositories listed
  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).
• Dink-Net: Neural Clustering on Large Graphs
  28 May 2023 2 repositories listed Syntology ran 2 of 17 samples · 15 unverified
  Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner.
• Deep Temporal Graph Clustering
  18 May 2023 2 repositories listed
  To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal…
• Hard Sample Aware Network for Contrastive Deep Graph Clustering
  16 Dec 2022 2 repositories listed
  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…
• GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
  7 Dec 2022 2 repositories listed
  During the training procedure, we notice the distinct optimization goals for training learnable augmentors and contrastive learning networks.
• A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
  23 Nov 2022 2 repositories listed Syntology ran 0 of 15 samples · 15 unverified
  However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
• RuDSI: graph-based word sense induction dataset for Russian
  28 Sep 2022 2 repositories listed
  We present RuDSI, a new benchmark for word sense induction (WSI) in Russian.
• NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering
  16 Jun 2022 2 repositories listed
  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…
• Attributed Graph Clustering with Dual Redundancy Reduction
  18 Apr 2022 2 repositories listed
  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.
• Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
  1 Jan 2022 2 repositories listed
  We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.
• Deep Graph Clustering via Dual Correlation Reduction
  29 Dec 2021 2 repositories listed
  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.
• Attention-driven Graph Clustering Network
  12 Aug 2021 2 repositories listed
  The combination of the traditional convolutional network (i.
• Generative hypergraph clustering: from blockmodels to modularity
  24 Jan 2021 2 repositories listed
  Many graph algorithms for this task are based on variants of the stochastic blockmodel, a random graph with flexible cluster structure.
• Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
  21 Jan 2021 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.
• Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
  16 Dec 2020 2 repositories listed
  We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.
• Laplacian Regularized Few-Shot Learning
  29 Jun 2020 2 repositories listed
  We propose a transductive Laplacian-regularized inference for few-shot tasks.
• Laplacian Regularized Few-Shot Learning
  28 Jun 2020 2 repositories listed Syntology ran 5 of 12 samples · 7 unverified · 7 pointer-only (licence)
  Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.
• Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
  18 Jun 2020 2 repositories listed
  Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances.
• p-Norm Flow Diffusion for Local Graph Clustering
  20 May 2020 2 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes…
• Flow-based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance
  20 Apr 2020 2 repositories listed
  Possible reasons for this are: the steep learning curve for these algorithms; the lack of efficient and easy to use software; and the lack of detailed numerical experiments on real-world data that demonstrate their…

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.