Graph Property Prediction
29 papers with code • 4 benchmarks • 2 datasets
Subtasks
Most implemented papers
Graph convolutions that can finally model local structure
Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles.
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.
Directed Acyclic Graph Neural Networks
Graph-structured data ubiquitously appears in science and engineering.
Identity-aware Graph Neural Networks
However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.
Molecular Representation Learning by Leveraging Chemical Information
As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph.
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
From this observation we derive "Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Weisfeiler and Lehman Go Cellular: CW Networks
Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).
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.
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
Explaining machine learning models is an important and increasingly popular area of research interest.
Embedding Graphs on Grassmann Manifold
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.