Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma

• URL: http://arxiv.org/abs/2412.09805v1
• Date: Fri, 13 Dec 2024 02:44:47 GMT
• Title: Universal Inceptive GNNs by Eliminating the Smoothness-generalization Dilemma
• Authors: Ming Gu, Zhuonan Zheng, Sheng Zhou, Meihan Liu, Jiawei Chen, Tanyu Qiao, Liangcheng Li, Jiajun Bu,
• Abstract summary: We propose an Inceptive Graph Neural Net-work (IGNN) that replaces the cascade dependency with an inceptive architecture. Our IGNN outperforms 23 baseline methods,demonstrating superior performance on both homophilic and het-erophilic graphs.
• Score: 14.493433090244078
• License:
• Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, such as transaction and social net-works. However, their application is often hindered by the varyinghomophily levels across different orders of neighboring nodes, ne-cessitating separate model designs for homophilic and heterophilicgraphs. In this paper, we aim to develop a unified framework ca-pable of handling neighborhoods of various orders and homophilylevels. Through theoretical exploration, we identify a previouslyoverlooked architectural aspect in multi-hop learning: the cascadedependency, which leads to asmoothness-generalization dilemma.This dilemma significantly affects the learning process, especiallyin the context of high-order neighborhoods and heterophilic graphs.To resolve this issue, we propose an Inceptive Graph Neural Net-work (IGNN), a universal message-passing framework that replacesthe cascade dependency with an inceptive architecture. IGNN pro-vides independent representations for each hop, allowing personal-ized generalization capabilities, and captures neighborhood-wiserelationships to select appropriate receptive fields. Extensive ex-periments show that our IGNN outperforms 23 baseline methods,demonstrating superior performance on both homophilic and het-erophilic graphs, while also scaling efficiently to large graphs.

Related papers

• Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification [6.963363358936621]
  We propose a universal node classification framework based on the Mixture-of-Experts (MoE) mechanism. GNNMoE significantly outperforms mainstream GNNs, heterophilous GNNs, and graph transformers in both node classification performance and universality across diverse graph datasets.
  arXiv  Detail & Related papers  (2025-02-12T03:10:26Z)
• Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification [4.129489934631072]
  Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. We propose GNNMoE, a universal model architecture for node classification. We show that GNNMoE performs exceptionally well across various types of graph data, effectively alleviating the over-smoothing issue and global noise.
  arXiv  Detail & Related papers  (2024-12-11T08:35:13Z)
• One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs [61.9759512646523]
  Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns. Existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset. We propose a novel cross-domain pretraining framework, "one model for one graph"
  arXiv  Detail & Related papers  (2024-11-30T01:49:45Z)
• 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)
• Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? [61.35457647107439]
  Most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns. We provide evidence that Graph Neural Networks(GNNs) on node classification typically perform admirably on homophilic nodes. We then propose a rigorous, non-i.i.d PAC-Bayesian generalization bound for GNNs, revealing reasons for the performance disparity.
  arXiv  Detail & Related papers  (2023-06-02T07:46:20Z)
• Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network [59.860534520941485]
  Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN) Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo.
  arXiv  Detail & Related papers  (2023-04-24T16:17:21Z)
• 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)
• 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)
• Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks [68.9026534589483]
  RioGNN is a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation.
  arXiv  Detail & Related papers  (2021-04-16T04:30:06Z)
• 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)

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.