Str-GCL: Structural Commonsense Driven Graph Contrastive Learning

• URL: http://arxiv.org/abs/2507.07141v1
• Date: Wed, 09 Jul 2025 03:41:48 GMT
• Title: Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
• Authors: Dongxiao He, Yongqi Huang, Jitao Zhao, Xiaobao Wang, Zhen Wang,
• Abstract summary: We propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL)<n>Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework.<n>To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL.
• Score: 12.717666156052342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations. However, current GCL methods primarily focus on capturing implicit semantic relationships, often overlooking the structural commonsense embedded within the graph's structure and attributes, which contains underlying knowledge crucial for effective representation learning. Due to the lack of explicit information and clear guidance in general graph, identifying and integrating such structural commonsense in GCL poses a significant challenge. To address this gap, we propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL). Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework. It introduces topological and attribute-based rules without altering the original graph and employs a representation alignment mechanism to guide the encoder in effectively capturing this commonsense. To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL. Extensive experiments demonstrate that Str-GCL outperforms existing GCL methods, providing a new perspective on leveraging structural commonsense in graph representation learning.

Related papers

• Graph Self-Supervised Learning with Learnable Structural and Positional Encodings [39.20899720477907]
  We introduce emphGenHopNet, a GNN framework that integrates a $k$-hop message-passing scheme.<n>We also propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process.<n>Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.
  arXiv  Detail & Related papers  (2025-02-22T14:10:06Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)
• Graph-level Protein Representation Learning by Structure Knowledge Refinement [50.775264276189695]
  This paper focuses on learning representation on the whole graph level in an unsupervised manner. We propose a novel framework called Structure Knowledge Refinement (SKR) which uses data structure to determine the probability of whether a pair is positive or negative.
  arXiv  Detail & Related papers  (2024-01-05T09:05:33Z)
• Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning [34.566003077992384]
  We present a systematic study of various graph contrastive learning (GCL) methods. By uncovering how the implicit inductive bias of GNNs works in contrastive learning, we theoretically provide insights into the above intriguing properties of GCL. Rather than directly porting existing NN methods to GCL, we advocate for more attention toward the unique architecture of graph learning.
  arXiv  Detail & Related papers  (2023-11-05T15:54:17Z)
• HomoGCL: Rethinking Homophily in Graph Contrastive Learning [64.85392028383164]
  HomoGCL is a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances. We show that HomoGCL yields multiple state-of-the-art results across six public datasets.
  arXiv  Detail & Related papers  (2023-06-16T04:06:52Z)
• Graph Contrastive Learning for Skeleton-based Action Recognition [85.86820157810213]
  We propose a graph contrastive learning framework for skeleton-based action recognition. SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative. SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current graph convolutional networks.
  arXiv  Detail & Related papers  (2023-01-26T02:09:16Z)
• 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)
• Uncovering the Structural Fairness in Graph Contrastive Learning [87.65091052291544]
  Graph contrastive learning (GCL) has emerged as a promising self-supervised approach for learning node representations. We show that representations obtained by GCL methods are already fairer to degree bias than those learned by GCN. We devise a novel graph augmentation method, called GRAph contrastive learning for DEgree bias (GRADE), which applies different strategies to low- and high-degree nodes.
  arXiv  Detail & Related papers  (2022-10-06T15:58:25Z)
• Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning [32.126228702554144]
  Graph Contrastive Learning (GCL) has drawn much research interest for learning generalizable, transferable, and robust node representations in a self-supervised fashion. In this work, we go beyond the existing unsupervised GCL counterparts and address their limitations by proposing a simple yet effective framework S$3$-CL. Our experiments demonstrate that the node representations learned by S$3$-CL achieve superior performance on different downstream tasks compared to the state-of-the-art GCL methods.
  arXiv  Detail & Related papers  (2022-02-17T07:20:09Z)

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.