1 code implementation • 25 Jun 2022 • Shichao Zhu, Chuan Zhou, Anfeng Cheng, Shirui Pan, Shuaiqiang Wang, Dawei Yin, Bin Wang
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
no code implementations • 1 Jun 2022 • Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
no code implementations • 28 Mar 2022 • Zimeng Li, Shichao Zhu, Bin Shao, Tie-Yan Liu, Xiangxiang Zeng, Tong Wang
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.
1 code implementation • 24 Jan 2022 • Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
1 code implementation • 6 Jun 2021 • Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
1 code implementation • NeurIPS 2020 • Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.