E-CGL：一种高效的持续图学习器
E-CGL: An Efficient Continual Graph Learner
AI总结：
针对持续图学习中数据相互依赖和大图效率低的问题，提出E-CGL方法，采用组合采样策略和与GCN共享权重的MLP模型，在性能超越基线的同时大幅降低遗忘并提升训练与推理速度。
摘要：
持续学习已成为从序列数据中学习同时保留已有知识的关键范式。在持续图学习领域——图会根据流式图数据持续演化——除了灾难性遗忘问题外，持续图学习还带来了独特的挑战，需要自适应且高效的图学习方法。第一个挑战源于不同图数据之间的相互依赖关系，即先前的图会影响新的数据分布。第二个挑战在于处理大图时的效率问题。为解决这两个问题，本文提出了一种高效持续图学习器（Efficient Continual Graph Learner，E-CGL）。我们通过验证重放策略的有效性，并引入一种同时考虑节点重要性和多样性的组合采样策略，来解决相互依赖问题。为克服效率限制，E-CGL采用了一种简单但有效的MLP（多层感知机）模型，该模型在训练期间与GCN（图卷积网络）共享权重，通过规避计算成本高昂的消息传递过程实现加速。我们的方法在两种设置下的四个图持续学习数据集上全面超越了九个基线方法，同时E-CGL将灾难性遗忘问题大幅降低至平均-1.1%。此外，在四个数据集上，E-CGL实现了平均15.83倍的训练时间加速和4.89倍的推理时间加速。这些结果表明，E-CGL不仅能有效处理持续训练过程中不同图数据之间的相关性，还能提升在大图上进行持续学习的效率。代码已公开于https://github.com/aubreygjh/E-CGL。
英文摘要：
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs continuously evolve based on streaming graph data, continual graph learning presents unique challenges that require adaptive and efficient graph learning methods in addition to the problem of catastrophic forgetting. The first challenge arises from the interdependencies between different graph data, where previous graphs can influence new data distributions. The second challenge lies in the efficiency concern when dealing with large graphs. To addresses these two problems, we produce an Efficient Continual Graph Learner (E-CGL) in this paper. We tackle the interdependencies issue by demonstrating the effectiveness of replay strategies and introducing a combined sampling strategy that considers both node importance and diversity. To overcome the limitation of efficiency, E-CGL leverages a simple yet effective MLP model that shares weights with a GCN during training, achieving acceleration by circumventing the computationally expensive message passing process. Our method comprehensively surpasses nine baselines on four graph continual learning datasets under two settings, meanwhile E-CGL largely reduces the catastrophic forgetting problem down to an average of -1.1%. Additionally, E-CGL achieves an average of 15.83x training time acceleration and 4.89x inference time acceleration across the four datasets. These results indicate that E-CGL not only effectively manages the correlation between different graph data during continual training but also enhances the efficiency of continual learning on large graphs. The code is publicly available at https://github.com/aubreygjh/E-CGL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
