In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes per given task (e.g., link prediction). The algorithm is initialized using static node embeddings, which are then aligned over the representations of a node at different time points, and eventually adapted for the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental tasks of temporal link prediction and multi-label node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm shows performance improvements across many of the datasets and baselines and is found particularly effective for graphs that are less cohesive, with a lower clustering coefficient.

该论文提出了一种节点嵌入方法，通过学习时间图的节点和边的演变，利用一种时间节点嵌入模型进行不同的图形预测任务，并通过合并历史嵌入，优化每个特定任务来创建一个节点的时间嵌入。评估表明，与竞争基线和算法替代方案相比，我们的算法在许多数据集和基线上都显示出性能改进，并且在较不凝聚、具有较低聚类系数的图形中效果特别显著。

时序图上的节点嵌入