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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/guanchengwan/fgssl

공식 코드

Federated Graph Semantic and Structural Learning

https://github.com/guetye/anomaly_detection

공식 코드

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

https://github.com/guillaumejaume/edGNN

공식 코드

edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

https://github.com/guolab-cellmechanics/gnn-collective-cell-dynamics

공식 코드

Learning Dynamics from Multicellular Graphs with Deep Neural Networks

https://github.com/gvogiatzis/trafficgen

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Synthesizing Traffic Datasets using Graph Neural Networks

https://github.com/gxstudy/Graph-Neural-Networks-for-Image-Understanding-Based-on-Multiple-Cues

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Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases

https://github.com/haguettaz/cheblienet

공식 코드

ChebLieNet: Invariant Spectral Graph NNs Turned Equivariant by Riemannian Geometry on Lie Groups

https://github.com/hailieqh/3d-object-primitive-graph

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Single Image 3D Object Estimation with Primitive Graph Networks

https://github.com/hairlab/decefl

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DeceFL: A Principled Decentralized Federated Learning Framework

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

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

https://github.com/hala64/udp

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Detection and Defense of Unlearnable Examples

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

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Invertible Neural Networks for Graph Prediction

https://github.com/hang53/rcgrl

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Robust Causal Graph Representation Learning against Confounding Effects

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

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Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks

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

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3D Infomax improves GNNs for Molecular Property Prediction

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

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Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

https://github.com/haoxiangye/ppbrl

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On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning

https://github.com/haoyuan-l/fed_datamesh

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Empowering Data Mesh with Federated Learning

https://github.com/haoyuzhao123/soteriafl

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SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

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

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Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

https://github.com/hasan-rakibul/boolean-search-bib-abstract

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Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods

https://github.com/hashemghanem/Gradients_scarcity_graph_learning

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Gradient scarcity with Bilevel Optimization for Graph Learning

https://github.com/hazdzz/Converter

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Converting Transformers into DGNNs Form

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

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CellTypeGraph: A New Geometric Computer Vision Benchmark

https://github.com/hehengtao/amp_gnn

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

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

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

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

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Learning Tree Structures from Leaves For Particle Decay Reconstruction

https://github.com/hennyjie/ibgnn

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

https://github.com/henrium/molsets

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MolSets: Molecular Graph Deep Sets Learning for Mixture Property Modeling

https://github.com/heyuepeng/fedah

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FedAH: Aggregated Head for Personalized Federated Learning

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

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

https://github.com/hgh0545/NA2

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Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning

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

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

https://github.com/hharcolezi/ldp-fairness-impact

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(Local) Differential Privacy has NO Disparate Impact on Fairness

https://github.com/hharcolezi/ldp-protocols-mobility-cdrs

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Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning

https://github.com/hharcolezi/multi-freq-ldpy

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On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms

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