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Graph Regression

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하위 태스크: 0
평가표: 22
보존된 평가 기록: 239

The regression task is similar to graph classification but using different loss function and performance metric.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AGH-ML-and-Chemoinformatics-Group/peptides_molecular_fingerprints_classification

공식 코드

Molecular Fingerprints Are Strong Models for Peptide Function Prediction

https://github.com/Axeln78/Transferability-of-spectral-gnns

공식 코드

An Experimental Study of the Transferability of Spectral Graph Networks

https://github.com/Cortys/rankgnn

공식 코드

Ranking Structured Objects with Graph Neural Networks

https://github.com/GeorgeMLP/basis-invariance-synthetic-experiment

공식 코드

Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

https://github.com/HIPS/neural-fingerprint

공식 코드

Convolutional Networks on Graphs for Learning Molecular Fingerprints

https://github.com/LUOyk1999/GNNPlus

공식 코드

Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence

https://github.com/Microsoft/Graphormer

공식 코드

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/NSLab-CUK/S-CGIB

공식 코드

Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

https://github.com/PKU-ML/canonicalization

공식 코드

A Canonicalization Perspective on Invariant and Equivariant Learning

https://github.com/PML-UCF/pinn

공식 코드

Fleet Prognosis with Physics-informed Recurrent Neural Networks

https://github.com/PaddlePaddle/PaddleHelix

공식 코드

GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions

https://github.com/PetarV-/GAT

공식 코드

Graph Attention Networks

https://github.com/RomanBresson/KAGNN

공식 코드

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

https://github.com/Roy-Shubhajit/FIT-GNN

공식 코드

FIT-GNN: Faster Inference Time for GNNs Using Coarsening

https://github.com/Saro00/DGN

공식 코드

Directional Graph Networks

https://github.com/Tiiiger/SGC

공식 코드

Simplifying Graph Convolutional Networks

https://github.com/XiaoxinHe/Graph-ViT-MLPMixer

공식 코드

A Generalization of ViT/MLP-Mixer to Graphs

https://github.com/YuGuangWang/PAN

공식 코드

Path Integral Based Convolution and Pooling for Graph Neural Networks

https://github.com/aimat-lab/gcnn_keras

공식 코드

MEGAN: Multi-Explanation Graph Attention Network

https://github.com/aimat-lab/graph_attention_student

공식 코드

MEGAN: Multi-Explanation Graph Attention Network

https://github.com/awa59kst120df/graph_attention_student

공식 코드

MEGAN: Multi-Explanation Graph Attention Network

https://github.com/awslabs/dgl-lifesci/blob/master/python/dgllife/model/model_zoo/mgcn_predictor.py

공식 코드

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

https://github.com/benatorc/OTGNN

공식 코드

Optimal Transport Graph Neural Networks

https://github.com/bengutteridge/drew

공식 코드

DRew: Dynamically Rewired Message Passing with Delay

https://github.com/bgu-cs-vil/im-mpnn

공식 코드

Improving the Effective Receptive Field of Message-Passing Neural Networks

https://github.com/borgwardtlab/neuralwalker

공식 코드

Learning Long Range Dependencies on Graphs via Random Walks

https://github.com/brain-research/mpnn

공식 코드

Neural Message Passing for Quantum Chemistry

https://github.com/carlosinator/support-graphs

공식 코드

Next Level Message-Passing with Hierarchical Support Graphs

https://github.com/chen-cai-osu/mpnn-gt-connection

공식 코드

On the Connection Between MPNN and Graph Transformer

https://github.com/chennnM/GCNII

공식 코드

Simple and Deep Graph Convolutional Networks

https://github.com/chrsmrrs/sparsewl

공식 코드

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

https://github.com/cptq/SignNet-BasisNet

공식 코드

Sign and Basis Invariant Networks for Spectral Graph Representation Learning

https://github.com/cvignac/SMP

공식 코드

Building powerful and equivariant graph neural networks with structural message-passing

https://github.com/cyh1112/GraphNormalization

공식 코드

Learning Graph Normalization for Graph Neural Networks

https://github.com/czczup/gptrans

공식 코드

Graph Propagation Transformer for Graph Representation Learning

https://github.com/daniaherzalla/gnn-jamming-source-localization

공식 코드

Graph Neural Networks for Jamming Source Localization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.