Self-supervised Contrastive Attributed Graph Clustering

• URL: http://arxiv.org/abs/2110.08264v1
• Date: Fri, 15 Oct 2021 03:25:28 GMT
• Title: Self-supervised Contrastive Attributed Graph Clustering
• Authors: Wei Xia, Quanxue Gao, Ming Yang, Xinbo Gao
• Abstract summary: We propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC) In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, are designed for node representation learning. For the OOS nodes, SCAGC can directly calculate their clustering labels.
• Score: 110.52694943592974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attributed graph clustering, which learns node representation from node attribute and topological graph for clustering, is a fundamental but challenging task for graph analysis. Recently, methods based on graph contrastive learning (GCL) have obtained impressive clustering performance on this task. Yet, we observe that existing GCL-based methods 1) fail to benefit from imprecise clustering labels; 2) require a post-processing operation to get clustering labels; 3) cannot solve out-of-sample (OOS) problem. To address these issues, we propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC). In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, which aims to maximize the similarities of intra-cluster nodes while minimizing the similarities of inter-cluster nodes, are designed for node representation learning. Meanwhile, a clustering module is built to directly output clustering labels by contrasting the representation of different clusters. Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels. Extensive experimental results on four benchmark datasets have shown that SCAGC consistently outperforms 11 competitive clustering methods.

Related papers

• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering [25.350054742471816]
  We propose a novel deep graph-level clustering method called Uniform Deep Graph Clustering (UDGC) UDGC assigns instances evenly to different clusters and then scatters those clusters on unit hypersphere, leading to a more uniform cluster-level distribution and a slighter cluster collapse. Our empirical study on eight well-known datasets demonstrates that UDGC significantly outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-11-23T12:08:20Z)
• Generalized Category Discovery with Clustering Assignment Consistency [56.92546133591019]
  Generalized category discovery (GCD) is a recently proposed open-world task. We propose a co-training-based framework that encourages clustering consistency. Our method achieves state-of-the-art performance on three generic benchmarks and three fine-grained visual recognition datasets.
  arXiv  Detail & Related papers  (2023-10-30T00:32:47Z)
• Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
  We propose a new deep graph clustering method termed Reinforcement Graph Clustering. In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
  arXiv  Detail & Related papers  (2023-08-13T18:12:28Z)
• DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering [6.447863458841379]
  This study introduces a lightweight Graph Neural Network (GNN) to replace classical clustering methods. Unlike existing methods, our GNN takes both the pair-wise affinities between local image features and the raw features as input. We demonstrate how classical clustering objectives can be formulated as self-supervised loss functions for training an image segmentation GNN.
  arXiv  Detail & Related papers  (2022-12-12T12:31:46Z)
• GLCC: A General Framework for Graph-level Clustering [5.069852282550117]
  This paper studies the problem of graph-level clustering, which is a novel yet challenging task. We propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.
  arXiv  Detail & Related papers  (2022-10-21T11:08:10Z)
• Dual Contrastive Attributed Graph Clustering Network [6.796682703663566]
  We propose a generic framework called Dual Contrastive Attributed Graph Clustering Network (DCAGC) In DCAGC, by leveraging Neighborhood Contrast Module, the similarity of the neighbor nodes will be maximized and the quality of the node representation will be improved. All the modules of DCAGC are trained and optimized in a unified framework, so the learned node representation contains clustering-oriented messages.
  arXiv  Detail & Related papers  (2022-06-16T03:17:01Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.