33 citations · 73 across the 14 of their papers we have counts for
7 papers · 1 filter
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching
Tim Kaler, Alexandros-Stavros Iliopoulos, Philip Murzynowski +3
Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs, owing to the widespread use and success of GNNs in…
Memory-based Message Passing: Decoupling the Message for Propogation from Discrimination
Jie Chen, Weiqi Liu, Jian Pu
Message passing is a fundamental procedure for graph neural networks in the field of graph representation learning. Based on the homophily assumption, the current message passing a…
Directed Acyclic Graph Neural Networks
Veronika Thost, Jie Chen
Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they…
Generating a Doppelganger Graph: Resembling but Distinct
Yuliang Ji, Ru Huang, Jie Chen +1
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e.g., images and sound waves). With the…
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Chao Shang, Jie Chen, Jinbo Bi
Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a mult…
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…