Graph Property Prediction
23 papers with code • 4 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Graph Property Prediction
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OGB
PCQM4Mv2-LSC
Most implemented papers
How Attentive are Graph Attention Networks?
tech-srl/how_attentive_are_gats
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ICLR 2022
Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Optimization-AI/LibAUC
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ICCV 2021
Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks.
Do Transformers Really Perform Bad for Graph Representation?
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
DeeperGCN: All You Need to Train Deeper GCNs
dmlc/dgl
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Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
Global Self-Attention as a Replacement for Graph Convolution
shamim-hussain/egt_pytorch
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The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data.
Nested Graph Neural Networks
muhanzhang/nestedgnn
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NeurIPS 2021
The key is to make each node representation encode a subgraph around it more than a subtree.
Wasserstein Embedding for Graph Learning
navid-naderi/WEGL
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ICLR 2021
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks.
Improving Graph Property Prediction with Generalized Readout Functions
Graph property prediction is drawing increasing attention in the recent years due to the fact that graphs are one of the most general data structures since they can contain an arbitrary number of nodes and connections between them, and it is the backbone for many different tasks like classification and regression on such kind of data (networks, molecules, knowledge bases, ...).
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.
