Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective

• URL: http://arxiv.org/abs/2412.00742v1
• Date: Sun, 01 Dec 2024 09:33:20 GMT
• Title: Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
• Authors: Yujie Mo, Zhihe Lu, Runpeng Yu, Xiaofeng Zhu, Xinchao Wang,
• Abstract summary: Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. Existing SHGL methods encounter two significant limitations. We introduce a novel framework enhanced by rank and dual consistency constraints.
• Score: 52.662463893268225
• License:
• Abstract: Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two significant limitations: (i) noise in graph structures is often introduced during the message-passing process to weaken node representations, and (ii) cluster-level information may be inadequately captured and leveraged, diminishing the performance in downstream tasks. In this paper, we address these limitations by theoretically revisiting SHGL from the spectral clustering perspective and introducing a novel framework enhanced by rank and dual consistency constraints. Specifically, our framework incorporates a rank-constrained spectral clustering method that refines the affinity matrix to exclude noise effectively. Additionally, we integrate node-level and cluster-level consistency constraints that concurrently capture invariant and clustering information to facilitate learning in downstream tasks. We theoretically demonstrate that the learned representations are divided into distinct partitions based on the number of classes and exhibit enhanced generalization ability across tasks. Experimental results affirm the superiority of our method, showcasing remarkable improvements in several downstream tasks compared to existing methods.

Related papers

• Fast Disentangled Slim Tensor Learning for Multi-view Clustering [28.950845031752927]
  We propose a new approach termed fast Disdentangle Slim Learning (DSTL) for multi-view clustering. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Our proposed model is computationally efficient and can be solved effectively.
  arXiv  Detail & Related papers  (2024-11-12T09:57:53Z)
• 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)
• Spectral Clustering in Convex and Constrained Settings [0.0]
  We introduce a novel framework for seamlessly integrating pairwise constraints into semidefinite spectral clustering. Our methodology systematically extends the capabilities of semidefinite spectral clustering to capture complex data structures.
  arXiv  Detail & Related papers  (2024-04-03T18:50:14Z)
• Stable Cluster Discrimination for Deep Clustering [7.175082696240088]
  Deep clustering can optimize representations of instances (i.e., representation learning) and explore the inherent data distribution. The coupled objective implies a trivial solution that all instances collapse to the uniform features. In this work, we first show that the prevalent discrimination task in supervised learning is unstable for one-stage clustering. A novel stable cluster discrimination (SeCu) task is proposed and a new hardness-aware clustering criterion can be obtained accordingly.
  arXiv  Detail & Related papers  (2023-11-24T06:43:26Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Learning the Precise Feature for Cluster Assignment [39.320210567860485]
  We propose a framework which integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-11T04:08:54Z)
• 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)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)

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.