Graph Learning under Distribution Shifts: A Comprehensive Survey on
Domain Adaptation, Out-of-distribution, and Continual Learning
- URL: http://arxiv.org/abs/2402.16374v2
- Date: Thu, 7 Mar 2024 05:07:49 GMT
- Title: Graph Learning under Distribution Shifts: A Comprehensive Survey on
Domain Adaptation, Out-of-distribution, and Continual Learning
- Authors: Man Wu, Xin Zheng, Qin Zhang, Xiao Shen, Xiong Luo, Xingquan Zhu,
Shirui Pan
- Abstract summary: We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning.
We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning.
We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
- Score: 53.81365215811222
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph learning plays a pivotal role and has gained significant attention in
various application scenarios, from social network analysis to recommendation
systems, for its effectiveness in modeling complex data relations represented
by graph structural data. In reality, the real-world graph data typically show
dynamics over time, with changing node attributes and edge structure, leading
to the severe graph data distribution shift issue. This issue is compounded by
the diverse and complex nature of distribution shifts, which can significantly
impact the performance of graph learning methods in degraded generalization and
adaptation capabilities, posing a substantial challenge to their effectiveness.
In this survey, we provide a comprehensive review and summary of the latest
approaches, strategies, and insights that address distribution shifts within
the context of graph learning. Concretely, according to the observability of
distributions in the inference stage and the availability of sufficient
supervision information in the training stage, we categorize existing graph
learning methods into several essential scenarios, including graph domain
adaptation learning, graph out-of-distribution learning, and graph continual
learning. For each scenario, a detailed taxonomy is proposed, with specific
descriptions and discussions of existing progress made in distribution-shifted
graph learning. Additionally, we discuss the potential applications and future
directions for graph learning under distribution shifts with a systematic
analysis of the current state in this field. The survey is positioned to
provide general guidance for the development of effective graph learning
algorithms in handling graph distribution shifts, and to stimulate future
research and advancements in this area.
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