Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in real-world scenarios, it often faces the open set problem with the dynamically increased class set as the time passes by. This will bring two big challenges to the existing dynamic GNN methods: (i) How to dynamically propagate appropriate information in an open temporal graph, where new class nodes are often linked to old class nodes. This case will lead to a sharp contradiction. This is because typical GNNs are prone to make the embeddings of connected nodes become similar, while we expect the embeddings of these two interactive nodes to be distinguishable since they belong to different classes. (ii) How to avoid catastrophic knowledge forgetting over old classes when learning new classes occurred in temporal graphs. In this paper, we propose a general and principled learning approach for open temporal graphs, called OTGNet, with the goal of addressing the above two challenges. We assume the knowledge of a node can be disentangled into class-relevant and class-agnostic one, and thus explore a new message passing mechanism by extending the information bottleneck principle to only propagate class-agnostic knowledge between nodes of different classes, avoiding aggregating conflictive information. Moreover, we devise a strategy to select both important and diverse triad sub-graph structures for effective class-incremental learning. Extensive experiments on three real-world datasets of different domains demonstrate the superiority of our method, compared to the baselines.

翻译：时序图神经网络（GNNs）近年来日益受到关注，其通常假设节点类别集合是封闭的。然而，在现实场景中，随着时间推移，类别集合会动态增加，因此常面临开放集问题。这给现有动态GNN方法带来两大挑战：（i）如何在开放时序图中动态传播恰当信息——新类别节点常与旧类别节点存在连接，这将导致尖锐矛盾。这是因为典型GNN倾向于使相连节点的嵌入表示趋于相似，而我们希望这两个交互节点的嵌入可区分（因属于不同类别）；（ii）如何在学习时序图中出现的新类别时，避免对旧类别知识的灾难性遗忘。本文提出一种面向开放时序图的通用且规范的学习方法OTGNet，旨在解决上述两个挑战。我们假设节点知识可解耦为类别相关与类别无关两部分，并据此通过扩展信息瓶颈原则探索新消息传递机制——仅在不同类别节点间传播类别无关知识，避免聚合冲突信息。此外，我们设计了一种策略，选取重要且多样的三元组子图结构以实现有效的类别增量学习。在三个不同领域真实数据集上的大量实验表明，相较于基线方法，本方法具有优越性。