Real-life graph data often expands continually, rendering the learning of
graph neural networks (GNNs) on static graph data impractical. Graph continual
learning (GCL) tackles this problem by continually adapting GNNs to the
expanded graph of the current task while maintaining the performance over the
graph of previous tasks. Memory replay-based methods, which aim to replay data
of previous tasks when learning new tasks, have been explored as one principled
approach to mitigate the forgetting of the knowledge learned from the previous
tasks. In this paper we extend this methodology with a novel framework, called
Debiased Lossless Memory replay (DeLoMe). Unlike existing methods that sample
nodes/edges of previous graphs to construct the memory, DeLoMe learns small
lossless synthetic node representations as the memory. The learned memory can
not only preserve the graph data privacy but also capture the holistic graph
information, for which the sampling-based methods are not viable. Further,
prior methods suffer from bias toward the current task due to the data
imbalance between the classes in the memory data and the current data. A
debiased GCL loss function is devised in DeLoMe to effectively alleviate this
bias. Extensive experiments on four graph datasets show the effectiveness of
DeLoMe under both class- and task-incremental learning settings.

本文介绍了一种名为 DeLoMe 的新框架，它通过学习小型无损合成节点表示来构建记忆，以解决真实生活中图数据不断扩展的问题，并且在保持先前任务图的性能的同时，持续适应当前任务的扩展图。

使用去偏差无损记忆重播的图形持续学习

Graph Continual Learning with Debiased Lossless Memory Replay

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.

综述研究了图学习中的分布转变问题及解决方法，将现有的图学习方法分为图领域适应学习、图越域学习和图持续学习三种关键场景，详细讨论了这些场景下的现有进展，并对该领域进行了潜在应用和未来研究方向的分析。

图学习在分布偏移下的研究：领域自适应、离域和连续学习的综合调查

Graph Learning under Distribution Shifts: A Comprehensive Survey on  Domain Adaptation, Out-of-distribution, and Continual Learning

We investigate the replay buffer in rehearsal-based approaches for graph
continual learning (GCL) methods. Existing rehearsal-based GCL methods select
the most representative nodes for each class and store them in a replay buffer
for later use in training subsequent tasks. However, we discovered that
considering only the class representativeness of each replayed node makes the
replayed nodes to be concentrated around the center of each class, incurring a
potential risk of overfitting to nodes residing in those regions, which
aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach
heavily relies on a few replayed nodes to retain knowledge obtained from
previous tasks, involving the replayed nodes that have irrelevant neighbors in
the model training may have a significant detrimental impact on model
performance. In this paper, we propose a GCL model named DSLR, specifically, we
devise a coverage-based diversity (CD) approach to consider both the class
representativeness and the diversity within each class of the replayed nodes.
Moreover, we adopt graph structure learning (GSL) to ensure that the replayed
nodes are connected to truly informative neighbors. Extensive experimental
results demonstrate the effectiveness and efficiency of DSLR.

我们针对图连续学习（GCL）方法中的回放缓冲区展开了研究。我们提出了一种名为 DSLR 的 GCL 模型，通过考虑回放节点的类别代表性和类内多样性来解决回放缓冲区中节点集中和模型性能受损的问题，并采用图结构学习来确保回放节点与真正具有信息的邻居连接。大量的实验结果证明了 DSLR 的有效性和高效性。