Introducing Diminutive Causal Structure into Graph Representation Learning

• URL: http://arxiv.org/abs/2406.08709v1
• Date: Thu, 13 Jun 2024 00:18:20 GMT
• Title: Introducing Diminutive Causal Structure into Graph Representation Learning
• Authors: Hang Gao, Peng Qiao, Yifan Jin, Fengge Wu, Jiangmeng Li, Changwen Zheng,
• Abstract summary: We introduce a novel method that enables Graph Neural Networks (GNNs) to glean insights from specialized diminutive causal structures. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures.
• Score: 19.132025125620274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable challenge for the model in accurately capturing authentic data relationships. A proposed mitigating strategy involves the direct integration of rules or relationships corresponding to the graph data into the model. However, within the domain of graph representation learning, the inherent complexity of graph data obstructs the derivation of a comprehensive causal structure that encapsulates universal rules or relationships governing the entire dataset. Instead, only specialized diminutive causal structures, delineating specific causal relationships within constrained subsets of graph data, emerge as discernible. Motivated by empirical insights, it is observed that GNN models exhibit a tendency to converge towards such specialized causal structures during the training process. Consequently, we posit that the introduction of these specific causal structures is advantageous for the training of GNN models. Building upon this proposition, we introduce a novel method that enables GNN models to glean insights from these specialized diminutive causal structures, thereby enhancing overall performance. Our method specifically extracts causal knowledge from the model representation of these diminutive causal structures and incorporates interchange intervention to optimize the learning process. Theoretical analysis serves to corroborate the efficacy of our proposed method. Furthermore, empirical experiments consistently demonstrate significant performance improvements across diverse datasets.

Related papers

• Heterophilic Graph Neural Networks Optimization with Causal Message-passing [24.796935814432892]
  We use causal inference to capture heterophilic message-passing in Graph Neural Network (GNN) We propose CausalMP, a causal message-passing discovery network for heterophilic graph learning.
  arXiv  Detail & Related papers  (2024-11-21T03:59:07Z)
• Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation [7.3491970177535]
  This study proposes a scheme to process graph structure data by combining graph neural network (GNN) The results show that the graph-based RAG model proposed in this paper is superior to the traditional generation model in terms of quality, knowledge consistency, and reasoning ability.
  arXiv  Detail & Related papers  (2024-11-06T00:23:55Z)
• Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks [16.12856816023414]
  This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data.
  arXiv  Detail & Related papers  (2024-10-23T07:14:37Z)
• Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting [50.181824673039436]
  We propose a Graph Structure Self-Contrasting (GSSC) framework that learns graph structural information without message passing. The proposed framework is based purely on Multi-Layer Perceptrons (MLPs), where the structural information is only implicitly incorporated as prior knowledge. It first applies structural sparsification to remove potentially uninformative or noisy edges in the neighborhood, and then performs structural self-contrasting in the sparsified neighborhood to learn robust node representations.
  arXiv  Detail & Related papers  (2024-09-09T12:56:02Z)
• CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Improving Temporal Knowledge Graph Extrapolation Reasoning [1.6795461001108096]
  We propose an innovative causal enhanced graph representation learning framework for temporal knowledge graph reasoning (TKGR) We first disentangle the evolutionary representations of entities and relations in a temporal graph sequence into two distinct components, namely causal representations and confounding representations.
  arXiv  Detail & Related papers  (2024-08-15T03:34:53Z)
• Rethinking Causal Relationships Learning in Graph Neural Networks [24.7962807148905]
  We introduce a lightweight and adaptable GNN module designed to strengthen GNNs' causal learning capabilities. We empirically validate the effectiveness of the proposed module.
  arXiv  Detail & Related papers  (2023-12-15T08:54:32Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)
• Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification [50.83222170524406]
  We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas'' We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
  arXiv  Detail & Related papers  (2020-06-29T01:01:42Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.