activity
20152021
most citedTri-graph Information Propagation for Polypharmacy Side Effect Prediction

11 citations · 20 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2021

Channel-Temporal Attention for First-Person Video Domain Adaptation

Xianyuan Liu, Shuo Zhou, Tao Lei +1

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recogniti…

cs.SI20201 cited

Unifying Homophily and Heterophily Network Transformation via Motifs

Yan Ge, Jun Ma, Li Zhang +1

Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network…

cs.LG20204 cited

Hop-Hop Relation-aware Graph Neural Networks

Li Zhang, Yan Ge, Haiping Lu

Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper,…

cs.LG202011 cited

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

Hao Xu, Shengqi Sang, Haiping Lu

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and protei…

cs.LG2018

Mixed-Order Spectral Clustering for Networks

Yan Ge, Haiping Lu, Pan Peng

Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g.,…

cs.CV2018

Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI

Wenwen Li, Jian Lou, Shuo Zhou +1

While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional st…