Representation Learning on Knowledge Graphs for Node Importance Estimation

In knowledge graphs, there are usually different types of nodes, multiple heterogeneous relations, and numerous attributes of nodes and edges, which impose the challenges on the task of Node Importance Estimation (NIE). Indeed, existing NIE approaches, such as PageRank (PR) and Node-Degree (ND), are not designed for handling knowledge graphs with the rich information related with these multifarious nodes and edges. To this end, in this paper, we propose a representation learning framework to leverage the rich information inherent in these multifarious nodes and edges for improving node importance estimation in knowledge graphs. Specifically, we provide a Relational Graph Transformer Network (RGTN), where a relational graph transformer is first proposed to propagate node information with the consideration of semantic predicate representations. Here, the assumption is that different predicates may have distinct effects on the transmission of node importance. Then, two separate encoders are designed to capture both the structural and semantic information of nodes respectively, and a co-attention module is developed to fuse the two separate representations of nodes. Next, an attention-based aggregation module is adopted to map the representations of nodes to their importance values. In addition, a learning-to-rank loss is designed to ensure that the learned representations can be aware of the relative ranking information among nodes. Finally, extensive experiments have been conducted on real-world knowledge graphs, and the results illustrate that our model outperforms the existing methods consistently for all the evaluation metrics. The code and the data are available at https://github.com/GRAPH-0/RGTN-NIE.