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：时序图上的图神经网络近来备受关注，其通常假设节点类别集合是封闭的。然而，现实场景中常面临开放集问题，即类别集合随时间动态增长。这给现有动态图神经网络方法带来两大挑战：(i) 如何在开放时序图中动态传播恰当信息——新类节点往往与旧类节点相连，导致尖锐矛盾：典型GNN倾向于使相连节点嵌入趋同，但这两类交互节点分属不同类别，其嵌入需具备可区分性；(ii) 如何在时序图中学习新类别时避免对旧类别的灾难性知识遗忘。本文提出一种面向开放时序图的通用原则化学习方法OTGNet，旨在解决上述两大挑战。我们假设节点知识可解耦为类别相关与类别无关两部分，通过扩展信息瓶颈原理设计新型消息传递机制——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，我们提出策略选择重要且多样的三元组子图结构，实现有效的类增量学习。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。