jinxinglu / graph-adversarial-learning-literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

Graph Adversarial Learning Literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

Papers are sorted by their uploaded dates in descending order.

Papers

Attack

Year	Title	Type	Target Task	Target Model	Venue	Link
2019	A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models	Attack	Node Classification	GCN, SGC	AAAI 2020	Link
2019	Time-aware Gradient Attack on Dynamic Network Link Prediction	Attack	Link Prediction	Dynamic Network Embedding Algs	Arxiv	Link
2019	Multiscale Evolutionary Perturbation Attack on Community Detection	Attack	Community Detection	Community Metrics	Arxiv	Link
2019	A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning	Attack	Regression, Classification	Label Propagation, Manifold Regularization	NeurIPS 2019	Link
2019	Attacking Graph Convolutional Networks via Rewiring	Attack	Node Classification	GCN	ICLR 2020 Openreview	Link
2019	Node Injection Attacks on Graphs via Reinforcement Learning	Attack	Node Classification	GCN	Arxiv	Link
2019	Unsupervised Euclidean Distance Attack on Network Embedding	Attack	Node Embedding	GCN	Arxiv	Link
2019	Generalizable Adversarial Attacks Using Generative Models	Attack	Node Classification	GCN	Arxiv	Link
2019	Vertex Nomination, Consistent Estimation, and Adversarial Modification	Attack	Vertex Nomination	VN Scheme	Arxiv	Link
2019	Towards Data Poisoning Attack against Knowledge Graph Embedding	Attack	Fact Plausibility Prediction	TransE, TransR	IJCAI 2019	Link
2018	Adversarial Attacks on Node Embeddings via Graph Poisoning	Attack	Node Classification, Community Detection	node2vec, DeepWalk, GCN, LINE	ICML 2019	Link
2019	Attacking Graph-based Classification via Manipulating the Graph Structure	Attack	Node Classification	Belief Propagation, GCN	CCS 2019	Link
2019	Adversarial Attacks on Graph Neural Networks via Meta Learning	Attack	Node Classification	GCN, CLN, DeepWalk	ICLR 2019	Link
2018	GA Based Q-Attack on Community Detection	Attack	Community Detection	Modularity, Community Detection Alg	IEEE TCSS	Link
2018	Data Poisoning Attack against Unsupervised Node Embedding Methods	Attack	Link Prediction	LINE, DeepWalk	Arxiv	Link
2018	Attack Graph Convolutional Networks by Adding Fake Nodes	Attack	Node Classification	GCN	Arxiv	Link
2018	Link Prediction Adversarial Attack	Attack	Link Prediction	GAE, GCN	Arxiv	Link
2018	Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network	Attack	Link Prediction	Traditional Link Prediction Algs	Scientific Reports	Link
2018	Attacking Similarity-Based Link Prediction in Social Networks	Attack	Link Prediction	local&global similarity metrics	AAMAS 2019	Link
2018	Fast Gradient Attack on Network Embedding	Attack	Node Classification	GCN	Arxiv	Link
2018	Adversarial Attack on Graph Structured Data	Attack	Node/Graph Classification	GNN, GCN	ICML 2018	Link
2018	Adversarial Attacks on Neural Networks for Graph Data	Attack	Node Classification	GCN	KDD 2018	Link
2017	Practical Attacks Against Graph-based Clustering	Attack	Graph Clustering	SVD, node2vec, Community Detection Alg	CCS 2017	Link

Defense

Year	Title	Type	Target Task	Target Model	Venue	Link
2019	GraphDefense: Towards Robust Graph Convolutional Networks	Defense	Node Classification	GCN	Arxiv	Link
2019	All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs	Defense			WSDM 2020	Link
2019	αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model	Defense	Malware Detection	HIN	CIKM 2019	Link
2019	Edge Dithering for Robust Adaptive Graph Convolutional Networks	Defense	Node Classification	GCN	Arxiv	Link
2019	GraphSAC: Detecting anomalies in large-scale graphs	Defense	Anomaly Detection	Anomaly Detection Algs	Arxiv	Link
2019	Certifiable Robustness to Graph Perturbations	Defense	Node Classification	GNN	NeurIPS 2019	Link
2019	Power up! Robust Graph Convolutional Network based on Graph Powering	Defense	Node Classification	GCN	ICLR 2020 Openreview	Link
2019	Adversarial Robustness of Similarity-Based Link Prediction	Defense	Link Prediction	local similarity metrics	ICDM 2019	Link
2019	Transferring Robustness for Graph Neural Network Against Poisoning Attacks	Defense	Node Classification	GNN	WSDM 2020	Link
2019	Improving Robustness to Attacks Against Vertex Classification	Defense	Node Classification	GCN	KDD Workshop 2019	Link
2019	Certifiable Robustness and Robust Training for Graph Convolutional Networks	Defense	Node Classification	GCN	KDD 2019	Link
2019	Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective	Defense	Node Classification	GNN	IJCAI 2019	Link
2019	Adversarial Examples on Graph Data: Deep Insights into Attack and Defense	Defense	Node Classification	GCN	IJCAI 2019	Link
2019	Adversarial Defense Framework for Graph Neural Network	Defense	Node Classification	GCN, GraphSAGE	Arxiv	Link
2019	Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications	Defense	Link Prediction	Knowledge Graph Embedding	NAACL 2019	Link
2019	Robust Graph Convolutional Networks Against Adversarial Attacks	Defense	Node Classification	GCN	KDD 2019	Link
2019	Can Adversarial Network Attack be Defended?	Defense	Node Classification	GNN	Arxiv	Link
2019	Virtual Adversarial Training on Graph Convolutional Networks in Node Classification	Defense	Node Classification	GCN	PRCV	Link
2019	Comparing and Detecting Adversarial Attacks for Graph Deep Learning	Defense	Node Classification	GCN, GAT, Nettack	RLGM@ICLR 2019	Link
2019	Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure	Defense	Node Classification	GCN	Arxiv	Link
2018	Characterizing Malicious Edges targeting on Graph Neural Networks	Defense	Detected Added Edges	GNN, GCN	ICLR 2019 OpenReview	Link

Survey

Adversarial Attack and Defense on Graph Data: A Survey (Link)

@article{sun2018adversarial,
  title={Adversarial Attack and Defense on Graph Data: A Survey},
  author={Sun, Lichao and Wang, Ji and Yu, Philip S and Li, Bo},
  journal={arXiv preprint arXiv:1812.10528},
  year={2018}
}