Search Results for author: Shichao Zhu

Found 6 papers, 4 papers with code

Geometry Contrastive Learning on Heterogeneous Graphs

1 code implementation25 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.

Contrastive Learning Node Classification +3

Ultrahyperbolic Knowledge Graph Embeddings

no code implementations1 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.

Knowledge Graph Embeddings

Multi-View Substructure Learning for Drug-Drug Interaction Prediction

no code implementations28 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.

Multi-Graph Fusion Networks for Urban Region Embedding

1 code implementation24 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.

Crime Prediction

Pseudo-Riemannian Graph Convolutional Networks

1 code implementation6 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.

Graph Reconstruction Inductive Bias +2

Graph Geometry Interaction Learning

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.

Link Prediction Node Classification

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