Structure-Preserving Graph Representation Learning

• URL: http://arxiv.org/abs/2209.00793v1
• Date: Fri, 2 Sep 2022 02:49:19 GMT
• Title: Structure-Preserving Graph Representation Learning
• Authors: Ruiyi Fang, Liangjian Wen, Zhao Kang, Jianzhuang Liu
• Abstract summary: We propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. Our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features.
• Score: 43.43429108503634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features.

Related papers

• GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck [5.943641527857957]
  We propose a novel method named textitGlobal-augmented Graph Structure Learning (GaGSL) The key idea behind GaGSL is to learn a compact and informative graph structure for node classification tasks. Comprehensive evaluations across a range of datasets reveal the outstanding performance and robustness of GaGSL compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-07T01:23:48Z)
• 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)
• MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure [8.00268216176428]
  Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing and learning representations from graph-structured data. A crucial prerequisite for the outstanding performance of GNNs is the availability of complete graph information. This study proposes a mutual dual-stream graph neural network (MDS-GNN) which implements a mutual benefit learning between features and structure.
  arXiv  Detail & Related papers  (2024-08-09T03:42:56Z)
• Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks [0.9463895540925061]
  We introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN) It not only models the graph structure but also extracts generalized unsupervised features to enhance classification performance. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method.
  arXiv  Detail & Related papers  (2024-03-24T06:28:54Z)
• Graph Data Condensation via Self-expressive Graph Structure Reconstruction [7.4525875528900665]
  We introduce a novel framework named textbfGraph Data textbfCondensation via textbfSelf-expressive Graph Structure textbfReconstruction. Our method explicitly incorporates the original graph structure into the condensing process and captures the nuanced interdependencies between the condensed nodes.
  arXiv  Detail & Related papers  (2024-03-12T03:54:25Z)
• GraphEdit: Large Language Models for Graph Structure Learning [62.618818029177355]
  Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data. Existing GSL methods heavily depend on explicit graph structural information as supervision signals. We propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data.
  arXiv  Detail & Related papers  (2024-02-23T08:29:42Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Self-supervised Consensus Representation Learning for Attributed Graph [15.729417511103602]
  We introduce self-supervised learning mechanism to graph representation learning. We propose a novel Self-supervised Consensus Representation Learning framework. Our proposed SCRL method treats graph from two perspectives: topology graph and feature graph.
  arXiv  Detail & Related papers  (2021-08-10T07:53:09Z)
• 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)
• GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
  We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2020-07-07T16:51:39Z)

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.