# Link Prediction Adversarial Attack

@article{Chen2018LinkPA, title={Link Prediction Adversarial Attack}, author={Jinyin Chen and Ziqiang Shi and Yangyang Wu and Xuanheng Xu and Haibin Zheng}, journal={ArXiv}, year={2018}, volume={abs/1810.01110} }

Deep neural network has shown remarkable performance in solving computer vision and some graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep model has also been revealed by carefully designed adversarial examples generated by various adversarial attack methods. With the wider application of deep model in complex network analysis, in this paper we define and formulate the link prediction adversarial attack problem and put forward a novel… Expand

#### 13 Citations

Can Adversarial Network Attack be Defended?

- Computer Science, Physics
- ArXiv
- 2019

This paper proposes novel adversarial training strategies to improve GNNs' defensibility against attacks, and analytically investigates the robustness properties for GNN's granted by the use of smooth defense, and proposes two special smooth defense strategies: smoothing distillation and smoothing cross-entropy loss function. Expand

Adversarial Attack and Defense on Graph Data: A Survey

- Computer Science
- ArXiv
- 2018

This work systemically organize the considered works based on the features of each topic and provides a unified formulation for adversarialLearning on graph data which covers most adversarial learning studies on graph. Expand

Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study

- Computer Science, Mathematics
- ArXiv
- 2020

A comprehensive overview of existing graph adversarial attacks and the countermeasures is provided, categorize existing attacks and defenses, and review the corresponding state of the art methods. Expand

Adversarial Attack on Hierarchical Graph Pooling Neural Networks

- Computer Science, Mathematics
- ArXiv
- 2020

This paper proposes an adversarial attack framework for the vulnerability of the Hierarchical Graph Pooling (HGP) Neural Networks, which are advanced GNNs that perform very well in the graph classification in terms of prediction accuracy and designs a surrogate model that consists of convolutional and pooling operators to generate adversarial samples to fool the hierarchical GNN-based graph classification models. Expand

GraphAttacker: A General Multi-Task GraphAttack Framework

- Computer Science
- ArXiv
- 2021

The results show that GraphAttacker can achieve state-of-the-art attack performance on graph analysis tasks of node classification, graph classification, and link prediction and the novel Similarity Modification Rate (SMR) to quantify the similarity between nodes thus constrain the attack budget is proposed. Expand

Survey on graph embeddings and their applications to machine learning problems on graphs

- Medicine, Computer Science
- PeerJ Comput. Sci.
- 2021

This survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description, and presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs. Expand

A Game-Theoretic Algorithm for Link Prediction

- Computer Science, Physics
- ArXiv
- 2019

A new, quasi-local approach is proposed (i.e., one which considers nodes within some radius k) that combines generalised group closeness centrality and semivalue interaction indices and achieves very good results even when given a suboptimal radius k as a parameter. Expand

N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network

- Computer Science, Physics
- IEEE Transactions on Computational Social Systems
- 2019

A novel clustering recommender system based on node2vec technology and rich information network, namely, N2VSCDNNR, to solve the data sparsity problem in the network and the two-phase personalized recommendation to realize the personalized recommendation of items for each user is proposed. Expand

Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

- Computer Science, Medicine
- IEEE Transactions on Visualization and Computer Graphics
- 2021

A visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. Expand

Graph Ranking Auditing: Problem Definition and Fast Solutions

- Computer Science
- IEEE Transactions on Knowledge and Data Engineering
- 2021

This paper proposes to audit graph ranking by finding the influential graph elements (e.g., edges, nodes, attributes, and subgraphs) regarding their impact on the ranking results and formulate graph ranking auditing problem as quantifying the influence of graph elements on theranking results. Expand

#### References

SHOWING 1-10 OF 53 REFERENCES

Fast Gradient Attack on Network Embedding

- Computer Science, Physics
- ArXiv
- 2018

A framework to generate adversarial networks based on the gradient information in Graph Convolutional Network (GCN) is proposed, and the proposed FGA behaves better than some baseline methods, i.e., the network embedding can be easily disturbed by only rewiring few links, achieving state-of-the-art attack performance. Expand

Adversarial Attack on Graph Structured Data

- Computer Science, Mathematics
- ICML
- 2018

This paper proposes a reinforcement learning based attack method that learns the generalizable attack policy, while only requiring prediction labels from the target classifier, and uses both synthetic and real-world data to show that a family of Graph Neural Network models are vulnerable to adversarial attacks. Expand

Adversarial Attacks on Neural Networks for Graph Data

- Computer Science, Mathematics
- KDD
- 2018

This work introduces the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions, and generates adversarial perturbations targeting the node's features and the graph structure, taking the dependencies between instances in account. Expand

Adversarial Attacks on Node Embeddings

- Computer Science, Mathematics
- ICML 2019
- 2018

This work provides the first adversarial vulnerability analysis on the widely used family of methods based on random walks, derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. Expand

Adversarial Attacks on Node Embeddings via Graph Poisoning

- Computer Science
- ICML
- 2019

This work provides the first adversarial vulnerability analysis on the widely used family of methods based on random walks to derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. Expand

Explaining and Harnessing Adversarial Examples

- Computer Science, Mathematics
- ICLR
- 2015

It is argued that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Expand

DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

- Computer Science
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016

The DeepFool algorithm is proposed to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers, and outperforms recent methods in the task of computing adversarial perturbation and making classifiers more robust. Expand

node2vec: Scalable Feature Learning for Networks

- Computer Science, Mathematics
- KDD
- 2016

In node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks, a flexible notion of a node's network neighborhood is defined and a biased random walk procedure is designed, which efficiently explores diverse neighborhoods. Expand

Intriguing properties of neural networks

- Computer Science
- ICLR
- 2014

It is found that there is no distinction between individual highlevel units and random linear combinations of high level units, according to various methods of unit analysis, and it is suggested that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. Expand

The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures

- Computer Science
- Privacy Enhancing Technologies
- 2010

It is shown that a privacy conscious community can substantially disrupt community detection using only local knowledge even while facing up to the asymmetry of a completely knowledgeable mobile-adversary. Expand