597 citations · 632 across the 16 of their papers we have counts for
4 papers · 1 filter
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Bohang Zhang, Jingchu Gai, Yiheng Du +3
Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community. So far, GNN expressiveness has been primarily assessed via the Weisfeiler-…
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity
Pengyun Yue, Hanzhen Zhao, Cong Fang +4
With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up trainin…
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
Jianhao Wang, Jin Zhang, Haozhe Jiang +3
Recent offline meta-reinforcement learning (meta-RL) methods typically utilize task-dependent behavior policies (e.g., training RL agents on each individual task) to collect a mult…
A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
Bohang Zhang, Guhao Feng, Yiheng Du +2
Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs). While numerous architectures have been proposed, so far there…