Graph Property Prediction

29 papers with code • 4 benchmarks • 2 datasets

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Most implemented papers

Graph convolutions that can finally model local structure

RBrossard/GINEPLUS 30 Nov 2020

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

qslim/epcb-gnns 14 Dec 2020

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

vthost/DAGNN ICLR 2021

Graph-structured data ubiquitously appears in science and engineering.

Identity-aware Graph Neural Networks

snap-stanford/graphgym 25 Jan 2021

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

PaddlePaddle/PaddleHelix NA 2021

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

Namkyeong/NoisyNodes_Pytorch 15 Jun 2021

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

twitter-research/cwn NeurIPS 2021

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

qslim/gnn-spectrum 14 Dec 2021

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

shichangzh/gstarx 28 Jan 2022

Explaining machine learning models is an important and increasingly popular area of research interest.

Embedding Graphs on Grassmann Manifold

conf20/egg 30 May 2022

Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.