CARL-G: Clustering-Accelerated Representation Learning on Graphs

• URL: http://arxiv.org/abs/2306.06936v2
• Date: Mon, 31 Jul 2023 20:54:18 GMT
• Title: CARL-G: Clustering-Accelerated Representation Learning on Graphs
• Authors: William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao, Neil Shah, Evangelos E. Papalexakis
• Abstract summary: We propose a novel clustering-based framework for graph representation learning that uses a loss inspired by Cluster Validation Indices (CVIs) CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline.
• Score: 18.763104937800215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from realizing their full potential. For instance, contrastive methods typically require negative sampling, which is often computationally costly. While non-contrastive methods avoid this expensive step, most existing methods either rely on overly complex architectures or dataset-specific augmentations. In this paper, we ask: Can we borrow from classical unsupervised machine learning literature in order to overcome those obstacles? Guided by our key insight that the goal of distance-based clustering closely resembles that of contrastive learning: both attempt to pull representations of similar items together and dissimilar items apart. As a result, we propose CARL-G - a novel clustering-based framework for graph representation learning that uses a loss inspired by Cluster Validation Indices (CVIs), i.e., internal measures of cluster quality (no ground truth required). CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline. CARL-G also performs at par or better than baselines in node clustering and similarity search tasks, training up to 1,500x faster than the best-performing baseline. Finally, we also provide theoretical foundations for the use of CVI-inspired losses in graph representation learning.

Related papers

• Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance [25.5510013711661]
  We propose the Clustering-guided Curriculum Graph contrastive Learning (CCGL) framework. CCGL uses clustering entropy as the guidance of the following graph augmentation and contrastive learning. Experimental results demonstrate that CCGL has achieved excellent performance compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-22T02:18:47Z)
• 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)
• CLC: Cluster Assignment via Contrastive Representation Learning [9.631532215759256]
  We propose Contrastive Learning-based Clustering (CLC), which uses contrastive learning to directly learn cluster assignment. We achieve 53.4% accuracy on the full ImageNet dataset and outperform existing methods by large margins.
  arXiv  Detail & Related papers  (2023-06-08T07:15:13Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• 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)
• CGC: Contrastive Graph Clustering for Community Detection and Tracking [33.48636823444052]
  We develop CGC, a novel end-to-end framework for graph clustering. CGC learns node embeddings and cluster assignments in a contrastive graph learning framework. We extend CGC for time-evolving data, where temporal graph clustering is performed in an incremental learning fashion.
  arXiv  Detail & Related papers  (2022-04-05T17:34:47Z)
• ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering [52.491074276133325]
  We propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering.
  arXiv  Detail & Related papers  (2022-03-01T02:32:25Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Graph Contrastive Clustering [131.67881457114316]
  We propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering(GCC) method. Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features. On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
  arXiv  Detail & Related papers  (2021-04-03T15:32:49Z)
• Structured Graph Learning for Clustering and Semi-supervised Classification [74.35376212789132]
  We propose a graph learning framework to preserve both the local and global structure of data. Our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Our model is equivalent to a combination of kernel k-means and k-means methods under certain condition.
  arXiv  Detail & Related papers  (2020-08-31T08:41:20Z)

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.