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Graph Neural Network

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

https://github.com/abureyanahmed/gnn-mcts-steiner

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Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search

https://github.com/abureyanahmed/gnn-msts-sparsification

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Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search

https://github.com/ace-kaist/pignet2

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PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening

https://github.com/acecreamu/craquelure-graphs

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Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns

https://github.com/acoursey3/freeway-anomaly-data

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection

https://github.com/adacheng/product_taxonomy_expansion

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Learning What You Need from What You Did: Product Taxonomy Expansion with User Behaviors Supervision

https://github.com/adityasengar/ld-fpg

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Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

https://github.com/agave233/SIGN

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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

https://github.com/agoodge/lunar

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LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

https://github.com/ahmedesamy/leap

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Leap: Inductive Link Prediction via Learnable TopologyAugmentation

https://github.com/ahxt/graphconv_is_mixup

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On the Equivalence of Graph Convolution and Mixup

https://github.com/ai4reason/ATP_Proofs

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Learning Theorem Proving Components

https://github.com/ai4risk/antifraud

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Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

https://github.com/aicps/sg-collision-prediction

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Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

https://github.com/aikunyi/fouriergnn

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FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

https://github.com/ailab-unifi/da-graphtab

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Data augmentation on graphs for table type classification

https://github.com/aixwinnie/hetedp

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Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

https://github.com/akensert/molgraph

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MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras

https://github.com/albertlordsun/bhgnn-rt

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BHGNN-RT: Network embedding for directed heterogeneous graphs

https://github.com/alexandor91/se3-equi-graph-registration

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Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration

https://github.com/alexmonti19/dagnet

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DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

https://github.com/alexrsaff/gbds_project

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Predicting Citi Bike Demand Evolution Using Dynamic Graphs

https://github.com/alge24/ADA-UGNN

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A Unified View on Graph Neural Networks as Graph Signal Denoising

https://github.com/alge24/DyGNN

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Streaming Graph Neural Networks

https://github.com/alirezadizaji/graphroar

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On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions

https://github.com/allanchen95/gccad

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Graph Contrastive Learning for Anomaly Detection

https://github.com/alleinx/m3-impute

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M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation

https://github.com/allminerlab/etr

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Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning

https://github.com/amayags/biox-cpath

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BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

https://github.com/amayags/mustang

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MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images

https://github.com/amazon-research/co-with-gnns-example

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Combinatorial Optimization with Physics-Inspired Graph Neural Networks

https://github.com/amazon-research/tgl

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TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

https://github.com/amazon-science/convolution-matching

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Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training

https://github.com/amazon-science/dlwp-benchmark

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Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics

https://github.com/amazon-science/network-usable-info-framework

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NetInfoF Framework: Measuring and Exploiting Network Usable Information

https://github.com/amazon-science/page-link-path-based-gnn-explanation

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PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction

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