Tianyu Xiong
ABSTRACT
Link prediction is one of the most essential tasks in data mining. A lot of studies have shown great progress in homogeneous graph. However, besides recommendation system and knowledge graph, little research solves the problem of link prediction in heterogeneous graphs. The main cause behind the failure of link prediction in heterogeneous graphs is that the way of connecting nodes is different from that in homogeneous graphs. In this article, we come up with a new model, Pro-SEAL, since it originates from SEAL. We notice that original labeling trick only takes distance into consideration, ignoring that different kinds of nodes with same distance may contribute to varying degrees. With this in mind, we design a novel labeling trick and make improvement in the final GNN learning structure. We take both ground-truth label and distance into account since they both matter when edges link. It expands framework of graph neural network(GNN) in link prediction and enables it to better adapt to heterogeneous graph for the first time. Extensive experiments on eight real-world datasets show that our model has obtained great results compared with some classic and advanced model.
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Index Terms
- How ground-truth label helps link prediction in heterogeneous graphs
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