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Privacy Preserving

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

https://github.com/gear/gfnn

공식 코드

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

https://github.com/geminilight/hrl-acra

공식 코드

Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

https://github.com/geods/cats

공식 코드

CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches

https://github.com/geods/flee-gnn

공식 코드

FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows

https://github.com/geoelements/gns

공식 코드

GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling

https://github.com/geometric-intelligence/topobenchmark

공식 코드

TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks

https://github.com/geopanag/UMGNet

공식 코드

Uplift Modeling Under Limited Supervision

https://github.com/georgeberry/role-action-embeddings

공식 코드

Role action embeddings: scalable representation of network positions

https://github.com/gesy17/TGBully

공식 코드

Improving Cyberbully Detection with User Interaction

https://github.com/ggchen1997/steps_bioinformatics

공식 코드

Structure-aware Protein Self-supervised Learning

https://github.com/ghanning/gcsa

공식 코드

Visual Re-Ranking with Non-Visual Side Information

https://github.com/giannisnik/pi-GNN

공식 코드

Permute Me Softly: Learning Soft Permutations for Graph Representations

https://github.com/gidariss/wDAE_GNN_FewShot

공식 코드

Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

https://github.com/giemp95/feddti

공식 코드

A Federated Learning Benchmark for Drug-Target Interaction

https://github.com/gim4855744/efi-gnn

공식 코드

Explicit Feature Interaction-aware Graph Neural Networks

https://github.com/gingasan/sit3

공식 코드

Code Summarization with Structure-induced Transformer

https://github.com/git-disl/ESORICS20-CPL

공식 코드

Gradient-Leakage Resilient Federated Learning

https://github.com/githubg0/mvg

공식 코드

Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

https://github.com/gnnaskernel/gnnaskernel

공식 코드

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

https://github.com/gnns4hri/sngnnv2

공식 코드

A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation

https://github.com/google-research/agent-based-epidemic-sim

공식 코드

Risk score learning for COVID-19 contact tracing apps

https://github.com/google-research/google-research

공식 코드

Node-Level Differentially Private Graph Neural Networks

https://github.com/google-research/population-dynamics

공식 코드

General Geospatial Inference with a Population Dynamics Foundation Model

https://github.com/google-research/social_cascades

공식 코드

Recurrent Graph Neural Networks for Rumor Detection in Online Forums

https://github.com/google/gematria

공식 코드

GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation

https://github.com/gorgen2020/dvgnn

공식 코드

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting

https://github.com/gorgen2020/hspgnn

공식 코드

Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation

https://github.com/gorkaydemir/FTGN

공식 코드

Trajectory Forecasting on Temporal Graphs

https://github.com/gp1g/fedaa

공식 코드

FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

https://github.com/gpdwatkins/graph_colouring_with_RL

공식 코드

Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks

https://github.com/graph-com/pvgalm

공식 코드

Privately Learning from Graphs with Applications in Fine-tuning Large Language Models

https://github.com/graph-com/strurw

공식 코드

Structural Re-weighting Improves Graph Domain Adaptation

https://github.com/graphdetec/rf-gnn

공식 코드

RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection

https://github.com/graphpku/gnn-lf

공식 코드

Graph Neural Network with Local Frame for Molecular Potential Energy Surface

https://github.com/graphpku/jacobiconv

공식 코드

How Powerful are Spectral Graph Neural Networks

https://github.com/graphpku/pygho

공식 코드

PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network

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