Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs

#Zemin_Liu_(Zhejiang_University); #Vincent_W._Zheng_(Advanced_Digital_Sciences_Center); #Zhou_Zhao_(Zhejiang_University); #Zhao_Li_(Alibaba_Group); #Hongxia_Yang_(Alibaba_Group); #Minghui_Wu_(Zhejiang_University); #Jing_Ying_(Zhejiang_University);

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths.

In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.

heterogeneous graphの埋め込み

https://www.youtube.com/watch?v=coGEQEl5jcc

PDF url: https://dl.acm.org/authorize?N665780