Alternatives and similar repositories for grabnel:

Users that are interested in grabnel are comparing it to the libraries listed below

• zhaohan-xi / GraphBackdoor
  ☆53Updated 2 years ago
• mims-harvard / GNNGuard
  Defending graph neural networks against adversarial attacks (NeurIPS 2020)
  ☆60Updated last year
• zaixizhang / graphbackdoor
  A PyTorch implementation of "Backdoor Attacks to Graph Neural Networks" (SACMAT'21)
  ☆36Updated 3 years ago
• THUDM / tdgia
  code for paper TDGIA:Effective Injection Attacks on Graph Neural Networks (KDD 2021, research track)
  ☆18Updated 3 years ago
• THUDM / grb
  Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph M…
  ☆91Updated last year
• KaidiXu / GCN_ADV_Train
  Adversarial training for Graph Neural Networks
  ☆59Updated 3 years ago
• EdisonLeeeee / GreatX
  A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
  ☆85Updated 3 months ago
• TaoShuchang / G-NIA
  G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)
  ☆27Updated 3 years ago
• LFhase / GIA-HAO
  [ICLR 2022] Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
  ☆37Updated last year
• danielzuegner / gnn-meta-attack
  Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".
  ☆146Updated 3 years ago
• jumxglhf / G2A2C
  Official repository for AAAI'23 paper: Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinf…
  ☆24Updated 2 years ago
• sigeisler / robustness_of_gnns_at_scale
  This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).
  ☆28Updated last year
• NDS-VU / awesome-gnn-privacy
  This repository aims to provide links to works about privacy attacks and privacy preservation on graph data with Graph Neural Networks (G…
  ☆22Updated last year
• RinneSz / CLGA
  Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22
  ☆17Updated 2 years ago
• EdisonLeeeee / Awesome-Fair-Graph-Learning
  Paper List for Fair Graph Learning (FairGL).
  ☆135Updated 4 months ago
• Hanjun-Dai / graph_adversarial_attack
  Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)
  ☆127Updated 2 years ago
• ispamm / FairDrop
  ☆14Updated 3 years ago
• danielzuegner / nettack
  Implementation of the paper "Adversarial Attacks on Neural Networks for Graph Data".
  ☆219Updated 2 years ago
• qitianwu / GraphOOD-EERM
  The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"
  ☆84Updated 2 years ago
• SwiftieH / GFAttack
  A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
  ☆36Updated 3 years ago
• wujcan / GIF-torch
  ☆22Updated 2 years ago
• BUPT-GAMMA / RoHe
  ☆19Updated 2 years ago
• VITA-Group / Unified-LTH-GNN
  [ICML 2021] "A Unified Lottery Tickets Hypothesis for Graph Neural Networks", Tianlong Chen*, Yongduo Sui*, Xuxi Chen, Aston Zhang, Zhang…
  ☆64Updated last year
• ventr1c / UGBA
  An official PyTorch implementation of "Unnoticeable Backdoor Attacks on Graph Neural Networks" (WWW 2023)
  ☆56Updated last year
• Radical3-HeZhang / Awesome-Trustworthy-GNNs
  ☆97Updated 6 months ago
• EdisonLeeeee / GraphData
  A collection of graph data used for semi-supervised node classification.
  ☆38Updated 2 years ago
• thupchnsky / sgc_unlearn
  Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)
  ☆19Updated last year
• brandeis-machine-learning / FairAdj
  Code for ICLR'2021 paper: On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
  ☆12Updated 3 years ago
• ChandlerBang / awesome-graph-attack-papers
  Adversarial attacks and defenses on Graph Neural Networks.
  ☆373Updated 10 months ago
• EdisonLeeeee / RS-Adversarial-Learning
  A curated collection of adversarial attack and defense on recommender systems.
  ☆133Updated 2 years ago