Link-aware link prediction over temporal graph by pattern recognition
This work addresses link prediction for temporal graphs, offering an interpretable method that improves over previous node-focused approaches, though it appears incremental in its focus on link-aware modeling.
The paper tackles the problem of link prediction on temporal graphs by proposing a link-aware model that uses both historical links and the query link to identify reasonable patterns, achieving strong performance on six datasets compared to state-of-the-art baselines.
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: https://github.com/lbq8942/TGACN.