Researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired by recently powerful graph mining methods like skip-gram models and Graph Neural Networks (GNNs), existing approaches focus on generating temporal node embeddings sequentially with nodes' sequential interactions. However, the sequential modeling of previous approaches cannot handle the transition structure between nodes' neighbors with limited memorization capacity. Detailedly, an effective method for the transition structures is required to both model nodes' personalized patterns adaptively and capture node dynamics accordingly. In this paper, we propose a method, namely Transition Propagation Graph Neural Networks (TIP-GNN), to tackle the challenges of encoding nodes' transition structures. The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph. Based on the bilevel graph, TIP-GNN further encodes transition structures by multi-step transition propagation and distills information from neighborhoods by a bilevel graph convolution. Experimental results over various temporal networks reveal the efficiency of our TIP-GNN, with at most 7.2\% improvements of accuracy on temporal link prediction. Extensive ablation studies further verify the effectiveness and limitations of the transition propagation module. Our code is available at \url{https://github.com/doujiang-zheng/TIP-GNN}.

翻译：时序网络（如社交网络与交易网络）的研究人员一直致力于从节点多样化的交互中挖掘其动态模式。受近期强大的图挖掘方法（如skip-gram模型和图神经网络）启发，现有方法专注于通过节点的顺序交互迭代生成时序节点嵌入。然而，以往方法的顺序建模方式受限于有限的记忆容量，难以处理节点邻域间的迁移结构。具体而言，设计针对迁移结构的有效方法需要既能自适应地建模节点的个性化模式，又能相应地捕获节点动态。本文提出一种名为迁移传播图神经网络（Transition Propagation Graph Neural Networks, TIP-GNN）的方法，以应对编码节点迁移结构的挑战。所提出的TIP-GNN聚焦于时序网络中的双层图结构：除了显式的交互图外，节点的顺序交互还可构建为迁移图。基于该双层图，TIP-GNN通过多步迁移传播编码迁移结构，并利用双层图卷积从邻域中提取信息。在多个时序网络上的实验结果表明，我们的TIP-GNN具有高效性，在时序链接预测任务上的准确率最高提升7.2%。广泛的消融研究进一步验证了迁移传播模块的有效性与局限性。我们的代码开源于\url{https://github.com/doujiang-zheng/TIP-GNN}。