On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks

• URL: http://arxiv.org/abs/2409.17475v1
• Date: Thu, 26 Sep 2024 02:19:48 GMT
• Title: On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
• Authors: Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra,
• Abstract summary: Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models. We focus on the link prediction task and systematically analyze the impact of heterophily in node features on GNN performance.
• Score: 12.26334940017605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models. While the challenges of applying GNNs for node classification when class labels display strong heterophily are well understood, it is unclear how heterophily affects GNN performance in other important graph learning tasks where class labels are not available. In this work, we focus on the link prediction task and systematically analyze the impact of heterophily in node features on GNN performance. Theoretically, we first introduce formal definitions of homophilic and heterophilic link prediction tasks, and present a theoretical framework that highlights the different optimizations needed for the respective tasks. We then analyze how different link prediction encoders and decoders adapt to varying levels of feature homophily and introduce designs for improved performance. Our empirical analysis on a variety of synthetic and real-world datasets confirms our theoretical insights and highlights the importance of adopting learnable decoders and GNN encoders with ego- and neighbor-embedding separation in message passing for link prediction tasks beyond homophily.

Related papers

• Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance [0.5416466085090772]
  We introduce a comprehensive benchmarking framework for graph machine learning. We generate synthetic networks with realistic topological properties and node feature vectors. Results highlight the dependency of model performance on the interplay between network structure and node features.
  arXiv  Detail & Related papers  (2024-06-04T20:40:06Z)
• Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network [4.078409998614025]
  Heterophily, nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. We propose and demonstrate that the valuable semantic information inherent in heterophily can be utilized effectively in graph learning. We propose HiGNN, an innovative approach that constructs an additional new graph structure, that integrates heterophilous information by leveraging node distribution.
  arXiv  Detail & Related papers  (2024-03-26T03:29:42Z)
• Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias [75.44877675117749]
  We propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph datasets Benchmark.
  arXiv  Detail & Related papers  (2023-09-26T13:09:43Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Graph Neural Networks for Graphs with Heterophily: A Survey [98.45621222357397]
  We provide a comprehensive review of graph neural networks (GNNs) for heterophilic graphs. Specifically, we propose a systematic taxonomy that essentially governs existing heterophilic GNN models. We discuss the correlation between graph heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs.
  arXiv  Detail & Related papers  (2022-02-14T23:07:47Z)
• Is Homophily a Necessity for Graph Neural Networks? [50.959340355849896]
  Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. GNNs are widely believed to work well due to the homophily assumption ("like attracts like"), and fail to generalize to heterophilous graphs where dissimilar nodes connect. Recent works design new architectures to overcome such heterophily-related limitations, citing poor baseline performance and new architecture improvements on a few heterophilous graph benchmark datasets as evidence for this notion. In our experiments, we empirically find that standard graph convolutional networks (GCNs) can actually achieve better performance than
  arXiv  Detail & Related papers  (2021-06-11T02:44:00Z)
• Graph Neural Networks with Heterophily [40.23690407583509]
  We propose a novel framework called CPGNN that generalizes GNNs for graphs with either homophily or heterophily. We show that replacing the compatibility matrix in our framework with the identity (which represents pure homophily) reduces to GCN.
  arXiv  Detail & Related papers  (2020-09-28T18:29:36Z)
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs [28.77753005139331]
  We investigate the representation power of graph neural networks in a semi-supervised node classification task under heterophily or low homophily. Many popular GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure. We identify a set of key designs that boost learning from the graph structure under heterophily.
  arXiv  Detail & Related papers  (2020-06-20T02:05:01Z)
• Adaptive Universal Generalized PageRank Graph Neural Network [36.850433364139924]
  Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility. We introduce a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights. GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.
  arXiv  Detail & Related papers  (2020-06-14T19:27:39Z)

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.