Interactive Paths Embedding For Semantic Proximity Search On Heterogeneous Graphs

Authors:
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

Introduction:

This paper studies Semantic proximity search on heterogeneous graph. The authors introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. They then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation.

Abstract:

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.

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