458 citations · 979 across the 26 of their papers we have counts for
7 papers · 1 filter
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Tengfei Ma, Jie Chen
Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby…
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Tengfei Ma, Junyuan Shang, Cao Xiao +1
Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is one of the most important tasks in drug development and medical practice. Recently graph neural networks…
DeepDrawing: A Deep Learning Approach to Graph Drawing
Yong Wang, Zhihua Jin, Qianwen Wang +3
Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algor…
Pre-training of Graph Augmented Transformers for Medication Recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao +1
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal elec…
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Patrick Ferber, Tengfei Ma, Siyu Huo +2
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Compe…
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Shenda Hong, Cao Xiao, Tengfei Ma +2
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however t…