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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/xiwenc1/incident-gnn-cp

공식 코드

Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis

https://github.com/xjtuwgt/gnn-magna

공식 코드

Multi-hop Attention Graph Neural Network

https://github.com/xlx0010/hgnn

공식 코드

Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation

https://github.com/xmh1011/at-dgnn

공식 코드

MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning

https://github.com/xpgogogo/sccdcg

공식 코드

scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding

https://github.com/xptree/DeepInf

공식 코드

DeepInf: Social Influence Prediction with Deep Learning

https://github.com/xuexue/neuralkanren

공식 코드

Neural Guided Constraint Logic Programming for Program Synthesis

https://github.com/xuhaocuhk/TilinGNN

공식 코드

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network

https://github.com/xun468/Time-GNN

공식 코드

TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting

https://github.com/xuyankun/stvig-for-reads-v

공식 코드

VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG

https://github.com/xuyankun/vsvig

공식 코드

VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG

https://github.com/xxhu94/csgnn

공식 코드

Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection

https://github.com/xxhu94/gat-cobo

공식 코드

GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection

https://github.com/xy2119/Causal_Knowledge_GNN

공식 코드

Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

https://github.com/xydong127/rqgnn

공식 코드

Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection

https://github.com/xydong127/spacegnn

공식 코드

SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels

https://github.com/xygaog/cgc

공식 코드

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

https://github.com/xzenglab/resources-for-ddis-prediction-using-dl

공식 코드

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

https://github.com/yCobanoglu/infinite-width-gnns

공식 코드

Infinite Width Graph Neural Networks for Node Regression/ Classification

https://github.com/yanghu819/Graph-MLP

공식 코드

Graph-MLP: Node Classification without Message Passing in Graph

https://github.com/yangliangwei/dgrec

공식 코드

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

https://github.com/yangxin666/st-gtrend

공식 코드

Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale

https://github.com/yangysc/resinet

공식 코드

Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features

https://github.com/yanir75/gnn-anomalydetection

공식 코드

Cloudy with a Chance of Anomalies: Dynamic Graph Neural Network for Early Detection of Cloud Services' User Anomalies

https://github.com/yash-jakhmola/w-dstagnn

공식 코드

Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention

https://github.com/yasir-ali-farrukh/gnn4id

공식 코드

XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model

https://github.com/yasminsalehi/physgnn

공식 코드

PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery

https://github.com/yassinmihemedi/strong-transitivity-relations-and-graph-neural-network

공식 코드

Strong Transitivity Relations and Graph Neural Networks

https://github.com/yejunzhang/a2-gnn

공식 코드

A2-GNN: Angle-Annular GNN for Visual Descriptor-free Camera Relocalization

https://github.com/yellowshippo/penn-neurips2022

공식 코드

Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions

https://github.com/yeslab-code/htgnn

공식 코드

Heterogeneous Temporal Graph Neural Network

https://github.com/yeweiysh/gnd-nets

공식 코드

Graph Neural Diffusion Networks for Semi-supervised Learning

https://github.com/yf-he/unigraph2

공식 코드

UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs

https://github.com/yfqiu-nlp/hiergnn

공식 코드

Abstractive Summarization Guided by Latent Hierarchical Document Structure

https://github.com/yi-ding-cs/LGG

공식 코드

LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

https://github.com/yimengmin/UTSP

공식 코드

Unsupervised Learning for Solving the Travelling Salesman Problem

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