Robust Graph Structure Learning under Heterophily

• URL: http://arxiv.org/abs/2403.03659v1
• Date: Wed, 6 Mar 2024 12:29:13 GMT
• Title: Robust Graph Structure Learning under Heterophily
• Authors: Xuanting Xie, Zhao Kang, Wenyu Chen
• Abstract summary: We propose a novel robust graph structure learning method to achieve a high-quality graph from heterophilic data for downstream tasks. We first apply a high-pass filter to make each node more distinctive from its neighbors by encoding structure information into the node features. Then, we learn a robust graph with an adaptive norm characterizing different levels of noise.
• Score: 12.557639223778722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assume a given graph to be accurate and complete. However, real data is inevitably noisy and sparse, which will lead to inferior results. Despite the remarkable success of recent graph representation learning methods, they inherently presume that the graph is homophilic, and largely overlook heterophily, where most connected nodes are from different classes. In this regard, we propose a novel robust graph structure learning method to achieve a high-quality graph from heterophilic data for downstream tasks. We first apply a high-pass filter to make each node more distinctive from its neighbors by encoding structure information into the node features. Then, we learn a robust graph with an adaptive norm characterizing different levels of noise. Afterwards, we propose a novel regularizer to further refine the graph structure. Clustering and semi-supervised classification experiments on heterophilic graphs verify the effectiveness of our method.

Related papers

• Non-Homophilic Graph Pre-Training and Prompt Learning [11.996173149569627]
  We propose ProNoG, a novel pre-training and prompt learning framework for non-homophilic graphs. First, we analyze existing graph pre-training methods, providing theoretical insights into the choice of pre-training tasks. Second, recognizing that each node exhibits unique non-homophilic characteristics, we propose a conditional network to characterize the node-specific patterns in downstream tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:57:31Z)
• HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs [13.01983932286923]
  We propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) HeteroMILE repeatedly coarsens the large sized graph into a smaller size while preserving the backbone structure of the graph before embedding it. It then refines the coarsened embedding to the original graph using a heterogeneous graph convolution neural network.
  arXiv  Detail & Related papers  (2024-03-31T22:22:10Z)
• Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering [29.17784041837907]
  We propose Adaptive Hybrid Graph Filter for Multi-View Graph Clustering (AHGFC) AHGFC learns the node embedding based on the graph joint aggregation matrix. Experimental results show that our proposed model performs well on six datasets containing homophilous and heterophilous graphs.
  arXiv  Detail & Related papers  (2024-01-05T07:27:29Z)
• Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views [1.4445779250002606]
  We propose a novel multiview contrastive learning approach that integrates diffusion filters on graphs. By incorporating multiple graph views as augmentations, our method captures the structural equivalence in heterophilic graphs.
  arXiv  Detail & Related papers  (2023-08-19T17:42:02Z)
• Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs [48.79929516665371]
  Graphs with homophily-prone edges tend to connect nodes with the same class. Heterophily-prone edges tend to build relationships between nodes with different classes. Existing GNNs only take the original graph during training.
  arXiv  Detail & Related papers  (2023-06-13T08:06:10Z)
• Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering [15.764819403555512]
  It is impossible to first identify a graph as homophilic or heterophilic before a suitable GNN model can be found. We propose a novel graph clustering method, which contains three key components: graph reconstruction, a mixed filter, and dual graph clustering network. Our method dominates others on heterophilic graphs.
  arXiv  Detail & Related papers  (2023-05-03T01:49:01Z)
• Spectral Augmentations for Graph Contrastive Learning [50.149996923976836]
  Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. We propose a set of well-motivated graph transformation operations to provide a bank of candidates when constructing augmentations for a graph contrastive objective.
  arXiv  Detail & Related papers  (2023-02-06T16:26:29Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)

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.