Self-Explainable Graph Neural Networks for Link Prediction
Huaisheng Zhu Dongsheng Luo Xianfeng Tang Junjie Xu Hui Liu Suhang Wang
Graph Neural Networks (GNNs) have achieved state-of-the-art performance for
link prediction. However, GNNs suffer from poor interpretability, which limits
their adoptions in critical scenarios that require knowing why certain links
are predicted. Despite various methods proposed for the explainability of GNNs,
most of them are post-hoc explainers developed for explaining node
classification. Directly adopting existing post-hoc explainers for explaining
link prediction is sub-optimal because: (i) post-hoc explainers usually adopt
another strategy or model to explain a target model, which could misinterpret
the target model; and (ii) GNN explainers for node classification identify
crucial subgraphs around each node for the explanation; while for link
prediction, one needs to explain the prediction for each pair of nodes based on
graph structure and node attributes. Therefore, in this paper, we study a novel
problem of self-explainable GNNs for link prediction, which can simultaneously
give accurate predictions and explanations. Concretely, we propose a new
framework and it can find various $K$ important neighbors of one node to learn
pair-specific representations for links from this node to other nodes. These
$K$ different neighbors represent important characteristics of the node and
model various factors for links from it. Thus, $K$ neighbors can provide
explanations for the existence of links. Experiments on both synthetic and
real-world datasets verify the effectiveness of the proposed framework for link
prediction and explanation.