- author: Zemin Liu ; Vincent W. Zheng ; Zhou Zhao ; Zhao Li ; Hongxia Yang ; Minghui Wu ; Jing Ying
- 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.
- keywords: Semantic Proximity Search; Heterogeneous Graph; Interactive Paths Embedding
- interpretation:
- pdf: paper
- code: code
- dataset: LinkedIn,Facebook,DBLP,Taobao
- ppt/video:
- curator: Mengya Ji