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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/hailieqh/3d-object-primitive-graph

공식 코드

Single Image 3D Object Estimation with Primitive Graph Networks

https://github.com/haiyaoyu/RTT-Indoor-localization-data-set

공식 코드

Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype

https://github.com/hamrel-cxu/invertible-graph-neural-network-ignn

공식 코드

Invertible Neural Networks for Graph Prediction

https://github.com/hang53/rcgrl

공식 코드

Robust Causal Graph Representation Learning against Confounding Effects

https://github.com/hanji-ucd/gnn-hetnet

공식 코드

Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks

https://github.com/hannesstark/3dinfomax

공식 코드

3D Infomax improves GNNs for Molecular Property Prediction

https://github.com/haolun-wu/til

공식 코드

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

https://github.com/harvard-cns/teal

공식 코드

Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

https://github.com/hashemghanem/Gradients_scarcity_graph_learning

공식 코드

Gradient scarcity with Bilevel Optimization for Graph Learning

https://github.com/hazdzz/Converter

공식 코드

Converting Transformers into DGNNs Form

https://github.com/hci-unihd/plant-celltype

공식 코드

CellTypeGraph: A New Geometric Computer Vision Benchmark

https://github.com/hehengtao/amp_gnn

공식 코드

Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

https://github.com/helloworldlty/muse-gnn

공식 코드

MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data

https://github.com/helmholtz-ai-energy/baumbauen

공식 코드

Learning Tree Structures from Leaves For Particle Decay Reconstruction

https://github.com/hennyjie/ibgnn

공식 코드

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

https://github.com/henrium/molsets

공식 코드

MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

https://github.com/hfut-mialab/slidegcd

공식 코드

SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification

https://github.com/hgh0545/NA2

공식 코드

Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning

https://github.com/hgh0545/graph-fraudster

공식 코드

Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning

https://github.com/hikopensource/davar-lab-ocr

공식 코드

VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

https://github.com/hilbert9221/INFUNE

공식 코드

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage

https://github.com/hipgraph/isplib

공식 코드

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations

https://github.com/histocartography/hact-net

공식 코드

Hierarchical Graph Representations in Digital Pathology

https://github.com/hit-ices/tnnls-mtgn

공식 코드

Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network

https://github.com/hku-medai/higt

공식 코드

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

https://github.com/hku-medai/mult-ehr

공식 코드

Multi-task Heterogeneous Graph Learning on Electronic Health Records

https://github.com/hkuds/dgnn

공식 코드

Disentangled Graph Social Recommendation

https://github.com/hkuds/graphaug

공식 코드

Graph Augmentation for Recommendation

https://github.com/hkuds/lightgcl

공식 코드

LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

https://github.com/hkuds/lightgnn

공식 코드

LightGNN: Simple Graph Neural Network for Recommendation

https://github.com/hkuds/selfgnn

공식 코드

SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation

https://github.com/hkust-knowcomp/eventground

공식 코드

EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs

https://github.com/hlr/drgn

공식 코드

Dynamic Relevance Graph Network for Knowledge-Aware Question Answering

https://github.com/hmthanh/GCAT

공식 코드

Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs

https://github.com/hope-rita/thlm

공식 코드

Pretraining Language Models with Text-Attributed Heterogeneous Graphs

https://github.com/hoseinzadeehsan/gait

공식 코드

Graph Neural Network Approach to Semantic Type Detection in Tables

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