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Found 5 papers, 5 papers with code
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e. g., filtering in Graph Fourier Transforms.
Ranked #4 on
Graph Regression
on ZINC
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
Task-relevant structures can be $localized$ or $sparse$ which are only involved in subgraphs or characterized by the interactions of subgraphs (a hierarchical perspective).
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and their effects on GNN performance.
Ranked #3 on
Graph Classification
on ENZYMES
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track
In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track.
Breaking the Expressive Bottlenecks of Graph Neural Networks
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
