A temporal graph can be considered as a stream of links, each of which
represents an interaction between two nodes at a certain time. On temporal
graphs, link prediction is a common task, which aims to answer whether the
query link is true or not. To do this task, previous methods usually focus on
the learning of representations of the two nodes in the query link. We point
out that the learned representation by their models may encode too much
information with side effects for link prediction because they have not
utilized the information of the query link, i.e., they are link-unaware. Based
on this observation, we propose a link-aware model: historical links and the
query link are input together into the following model layers to distinguish
whether this input implies a reasonable pattern that ends with the query link.
During this process, we focus on the modeling of link evolution patterns rather
than node representations. Experiments on six datasets show that our model
achieves strong performances compared with state-of-the-art baselines, and the
results of link prediction are interpretable. The code and datasets are
available on the project website: this https URL

通过利用查询链接的信息，我们提出了一种链接感知模型，可以识别时变图中可能存在的可靠模式，以改进链接预测的性能。在六个数据集上的实验证明，我们的模型表现优异，且链接预测结果可解释。