Information networks are ubiquitous and are ideal for modeling relational data. Networks being sparse and irregular, network embedding algorithms have caught the attention of many researchers, who came up with numerous embeddings algorithms in static networks. Yet in real life, networks constantly evolve over time. Hence, evolutionary patterns, namely how nodes develop itself over time, would serve as a powerful complement to static structures in embedding networks, on which relatively few works focus. In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes. In particular, we analyze evolutionary patterns with and without periodicity and design strategies correspondingly to model such patterns in time-frequency domains based on causal convolutions. In addition, we propose a temporal objective function which is optimized simultaneously with proximity ones such that both temporal and structural information are preserved. With the adequate modeling of temporal information, our model is able to outperform other competitive methods in various prediction tasks.

中文翻译：

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EPNE：进化模式保留网络嵌入

信息网络无处不在，是关系数据建模的理想选择。网络稀疏且不规则，网络嵌入算法引起了许多研究人员的关注，他们提出了许多静态网络中的嵌入算法。然而，在现实生活中，网络会随着时间不断发展。因此，进化模式，即节点如何随着时间的推移自我发展，将作为嵌入网络中静态结构的有力补充，而这方面的工作相对较少。在本文中，我们提出了 EPNE，一种时间网络嵌入模型，保留了节点局部结构的进化模式。特别是，我们分析了具有和不具有周期性的进化模式，并相应地设计策略以基于因果卷积在时频域中对此类模式进行建模。此外，我们提出了一个时间目标函数，它与邻近函数同时优化，以便保留时间和结构信息。通过对时间信息进行充分建模，我们的模型能够在各种预测任务中胜过其他竞争方法。