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adversarial-recommender-systems-survey

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.

attack_gcn_gad

BinarizedAttack for GCN-based graph anomaly detection.

binarizedattack

disassortativegraphs

Papers about developing DL methods on disassortative graphs

focusedcleaner

gaug

AAAI'21: Data Augmentation for Graph Neural Networks

gcn_anomalydetection

Code for Deep Anomaly Detection on Attributed Networks (SDM2019)

gdcl

Graph Debiased Contrastive Learning with Joint Representation Clustering

graph-adversarial-learning

A curated collection of adversarial attack and defense on graph data.

open-review-toolkit

Open Review Toolkit: Better books, Higher sales, Increased access to knowledge

orthogonalpgd

pro-gnn

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

secrecyattack

sgcn

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

shillingattack

sl-gad

A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection", TKDE-21

structack

Structural-aware attacks on GNNs

zhuyulin-tony.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes