HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views

• URL: http://arxiv.org/abs/2502.13277v1
• Date: Tue, 18 Feb 2025 20:57:56 GMT
• Title: HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
• Authors: Khaled Mohammed Saifuddin, Jonathan Shihao Ji, Esra Akbas,
• Abstract summary: Graph Contrastive Learning (GCL) has demonstrated remarkable effectiveness in improving graph representations. In this paper, we introduce HyperGCL, a novel multimodal GCL framework from a hypergraph perspective.
• Score: 1.4678959818041628
• License:
• Abstract: Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations (e.g., node dropping, edge perturbation, attribute masking) may result in the loss of task-relevant information and a lack of adaptability to diverse input data. Furthermore, the selection of negative samples remains rarely explored. In this paper, we introduce HyperGCL, a novel multimodal GCL framework from a hypergraph perspective. HyperGCL constructs three distinct hypergraph views by jointly utilizing the input graph's structure and attributes, enabling a comprehensive integration of multiple modalities in contrastive learning. A learnable adaptive topology augmentation technique enhances these views by preserving important relations and filtering out noise. View-specific encoders capture essential characteristics from each view, while a network-aware contrastive loss leverages the underlying topology to define positive and negative samples effectively. Extensive experiments on benchmark datasets demonstrate that HyperGCL achieves state-of-the-art node classification performance.

Related papers

• Tensor-Fused Multi-View Graph Contrastive Learning [12.412040359604163]
  Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data. Current GCL models face challenges with computational demands and limited feature utilization. We propose TensorMV-GCL, a novel framework that integrates extended persistent homology with GCL representations and facilitates multi-scale feature extraction.
  arXiv  Detail & Related papers  (2024-10-20T01:40:12Z)
• GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning [0.0]
  Graph representation learning has emerged as a powerful tool for preserving graph topology when mapping nodes to vector representations. Current graph neural network models face the challenge of requiring extensive labeled data. We propose Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning.
  arXiv  Detail & Related papers  (2024-09-12T03:09:05Z)
• LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs [22.322402072526927]
  Heterogeneous Graph Contrastive Learning (HGCL) usually requires pre-defined meta-paths. textsfLAMP integrates various meta-path sub-graphs into a unified and stable structure. textsfLAMP significantly outperforms existing state-of-the-art unsupervised models in terms of accuracy and robustness.
  arXiv  Detail & Related papers  (2024-09-10T08:27:39Z)
• Hypergraph Transformer for Semi-Supervised Classification [50.92027313775934]
  We propose a novel hypergraph learning framework, HyperGraph Transformer (HyperGT) HyperGT uses a Transformer-based neural network architecture to effectively consider global correlations among all nodes and hyperedges. It achieves comprehensive hypergraph representation learning by effectively incorporating global interactions while preserving local connectivity patterns.
  arXiv  Detail & Related papers  (2023-12-18T17:50:52Z)
• Subgraph Networks Based Contrastive Learning [5.736011243152416]
  Graph contrastive learning (GCL) can solve the problem of annotated data scarcity. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. We propose a novel framework called subgraph network-based contrastive learning (SGNCL)
  arXiv  Detail & Related papers  (2023-06-06T08:52:44Z)
• Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph [60.28340453547902]
  Graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss. Existing GCL approaches fail to provide strong performance guarantees. We implement the Single-Pass Graph Contrastive Learning method (SP-GCL) Empirically, the features learned by the SP-GCL can match or outperform existing strong baselines with significantly less computational overhead.
  arXiv  Detail & Related papers  (2022-11-20T07:18:56Z)
• Unifying Graph Contrastive Learning with Flexible Contextual Scopes [57.86762576319638]
  We present a self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short) Our algorithm builds flexible contextual representations with contextual scopes by controlling the power of an adjacency matrix. Based on representations from both local and contextual scopes, distL optimises a very simple contrastive loss function for graph representation learning.
  arXiv  Detail & Related papers  (2022-10-17T07:16:17Z)
• Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative [126.0985540285981]
  We apply the contrastive learning approach from images/graphs (we refer to it as HyperGCL) to improve generalizability of hypergraph neural networks. We fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three augmentation strategies from graph-structured data. We propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters.
  arXiv  Detail & Related papers  (2022-10-07T20:12:20Z)
• Heterogeneous Graph Contrastive Multi-view Learning [11.489983916543805]
  Graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. We propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets.
  arXiv  Detail & Related papers  (2022-10-01T10:53:48Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification [110.52328716130022]
  Video-based person re-identification (re-ID) is an important research topic in computer vision. We propose a novel graph-based framework, namely Multi-Granular Hypergraph (MGH) to better representational capabilities. 90.0% top-1 accuracy on MARS is achieved using MGH, outperforming the state-of-the-arts schemes.
  arXiv  Detail & Related papers  (2021-04-30T11:20:02Z)

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.