Out-Of-Distribution Generalization on Graphs: A Survey

• URL: http://arxiv.org/abs/2202.07987v1
• Date: Wed, 16 Feb 2022 10:59:06 GMT
• Title: Out-Of-Distribution Generalization on Graphs: A Survey
• Authors: Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
• Abstract summary: Graph machine learning has been extensively studied in both academia and industry. Most of the literature is built on the I.I.D. hypothesis, i.e., testing and training graph data are independent and identically distributed. To solve this problem, out-of-distribution (OOD) generalization on graphs has made great progress and attracted ever-increasing attention from the research community. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
• Score: 45.16337435648981
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the I.I.D. hypothesis, i.e., testing and training graph data are independent and identically distributed. However, this I.I.D. hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the I.I.D. hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Last but not least, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.

Related papers

• Subgraph Aggregation for Out-of-Distribution Generalization on Graphs [29.884717215947745]
  Out-of-distribution (OOD) generalization in Graph Neural Networks (GNNs) has gained significant attention. We propose a novel framework, SubGraph Aggregation (SuGAr), designed to learn a diverse set of subgraphs. Experiments on both synthetic and real-world datasets demonstrate that SuGAr outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-29T16:54:37Z)
• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts. Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. To provide a better understanding of the literature, we systematically categorize the existing models based on our proposed taxonomy.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• Parametric Graph Representations in the Era of Foundation Models: A Survey and Position [69.48708136448694]
  Graphs have been widely used in the past decades of big data and AI to model comprehensive relational data. Identifying meaningful graph laws can significantly enhance the effectiveness of various applications.
  arXiv  Detail & Related papers  (2024-10-16T00:01:31Z)
• Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs [22.561747395557642]
  We provide an up-to-date and forward-looking review of graph Out-Of-Distribution (OOD) adaptation methods. Based on our proposed taxonomy for graph OOD adaptation, we systematically categorize the existing methods according to their learning paradigm. We point out promising research directions and the corresponding challenges.
  arXiv  Detail & Related papers  (2024-02-17T00:40:12Z)
• Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective [45.352741792795186]
  We show that the graph self-attention mechanism and the decoupled architecture contribute positively to graph OOD generalization. We develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture.
  arXiv  Detail & Related papers  (2024-02-13T05:38:45Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal [57.8455911689554]
  Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs) It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
  arXiv  Detail & Related papers  (2022-12-12T08:40:04Z)
• Unbiased Graph Embedding with Biased Graph Observations [52.82841737832561]
  We propose a principled new way for obtaining unbiased representations by learning from an underlying bias-free graph. Based on this new perspective, we propose two complementary methods for uncovering such an underlying graph.
  arXiv  Detail & Related papers  (2021-10-26T18:44:37Z)

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.