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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/sunilkmaurya/FSGNN

공식 코드

Improving Graph Neural Networks with Simple Architecture Design

https://github.com/superlouis/NODE-SELECT

공식 코드

NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique

https://github.com/swsamleo/mlstgcn

공식 코드

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

https://github.com/swtheing/imc-gae

공식 코드

Inductive Matrix Completion Using Graph Autoencoder

https://github.com/syon-li/depwignn

공식 코드

DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text

https://github.com/taihasegawa/degnn

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DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise

https://github.com/tailvyuanliang/hgwavenet

공식 코드

HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction

https://github.com/takuo-h/GNN-for-OOKB

공식 코드

Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

https://github.com/talbenha/cell-tracker-gnn

공식 코드

Graph Neural Network for Cell Tracking in Microscopy Videos

https://github.com/tanfiona/causeeffectdetection

공식 코드

NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection

https://github.com/tangxianfeng/PA-GNN

공식 코드

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

https://github.com/tanwimallick/TL-DCRNN

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Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

https://github.com/taowen0309/ttg-nn

공식 코드

Tensor-view Topological Graph Neural Network

https://github.com/tata1661/shine-emnlp21

공식 코드

Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification

https://github.com/tech-srl/bottleneck

공식 코드

On the Bottleneck of Graph Neural Networks and its Practical Implications

https://github.com/tedzhouhk/sedyt

공식 코드

SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings

https://github.com/temp-gimlab/gnnsampler

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GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware

https://github.com/tencent-ailab/DrugOOD

공식 코드

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

https://github.com/tencent-alchemy/Alchemy

공식 코드

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

https://github.com/tgebhart/sheaf_kg_transind

공식 코드

Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference

https://github.com/tharaka-perera/flex-net

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Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks

https://github.com/thejonaslab/vonmises-icml-2023

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Von Mises Mixture Distributions for Molecular Conformation Generation

https://github.com/thisisforreview/svd-gcn

공식 코드

A Simple Yet Effective SVD-GCN for Directed Graphs

https://github.com/thudm/graphcad

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Graph Contrastive Learning for Anomaly Detection

https://github.com/thumnlab/nas-bench-graph

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NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

https://github.com/thunlp/GNNPapers

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Graph Neural Networks: A Review of Methods and Applications

https://github.com/thunlp/MuGNN

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Multi-Channel Graph Neural Network for Entity Alignment

https://github.com/thuwangcy/directau

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Towards Representation Alignment and Uniformity in Collaborative Filtering

https://github.com/tianshu-wang/pfs-gnn-bipartite

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Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy

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

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Graph Neural Networks for Jamming Source Localization

https://github.com/timd3/moco

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Moco: A Learnable Meta Optimizer for Combinatorial Optimization

https://github.com/timpostuvan/adagrid

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AdaGrid: Adaptive Grid Search for Link Prediction Training Objective

https://github.com/tjpaik/invariantrepresentation

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Invariant Representations of Embedded Simplicial Complexes

https://github.com/tkforgeron/ocppm

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HOEG: A New Approach for Object-Centric Predictive Process Monitoring

https://github.com/tkipf/c-swm

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Contrastive Learning of Structured World Models

https://github.com/tlmakinen/cosmicgraphs

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The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues

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