In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foster fair evaluation and recognize challenges in CL settings, several evaluation frameworks have been proposed, focusing mainly on the single- and multi-label classification task on euclidean data. However, these evaluation frameworks are not trivially applicable when the input data is graph-structured, as they do not consider the topological structure inherent in graphs. Existing continual graph learning (CGL) evaluation frameworks have predominantly focussed on single-label scenarios in the node classification (NC) task. This focus has overlooked the complexities of multi-label scenarios, where nodes may exhibit affiliations with multiple labels, simultaneously participating in multiple tasks. We develop a graph-aware evaluation (\agale) framework that accommodates both single-labeled and multi-labeled nodes, addressing the limitations of previous evaluation frameworks. In particular, we define new incremental settings and devise data partitioning algorithms tailored to CGL datasets. We perform extensive experiments comparing methods from the domains of continual learning, continual graph learning, and dynamic graph learning (DGL). We theoretically analyze \agale and provide new insights about the role of homophily in the performance of compared methods. We release our framework at https://github.com/Tianqi-py/AGALE.

翻译：近年来，持续学习技术在从流式数据中学习的同时保持跨序列任务知识方面取得了显著进展，尤其是在欧几里得数据领域。为促进公平评估并识别持续学习场景中的挑战，已有多个评估框架被提出，主要聚焦于欧几里得数据上的单标签和多标签分类任务。然而，当输入数据为图结构时，这些评估框架无法直接适用，因为它们未考虑图中固有的拓扑结构。现有的持续图学习评估框架主要集中于节点分类任务中的单标签场景，忽视了多标签场景的复杂性——在该场景中，节点可能同时属于多个标签并参与多个任务。我们开发了一个图感知评估框架（AGALE），该框架兼容单标签和多标签节点，弥补了先前评估框架的局限性。具体而言，我们定义了新的增量设置，并设计了针对持续图学习数据集的定制化数据划分算法。我们进行了广泛实验，比较了来自持续学习、持续图学习和动态图学习领域的方法。我们从理论上分析了AGALE，并针对同质性在方法性能中的作用提出了新见解。我们已在https://github.com/Tianqi-py/AGALE 发布该框架。