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

03/02/2020 ∙ by Wei Jin, et al. ∙ Michigan State University 24

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks. As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. Adversary can mislead GNNs to give wrong predictions by modifying the graph structure such as manipulating a few edges. This vulnerability has arisen tremendous concerns for adapting GNNs in safety-critical applications and has attracted increasing research attention in recent years. Thus, it is necessary and timely to provide a comprehensive overview of existing graph adversarial attacks and the countermeasures. In this survey, we categorize existing attacks and defenses, and review the corresponding state-of-the-art methods. Furthermore, we have developed a repository with representative algorithms ( The repository enables us to conduct empirical studies to deepen our understandings on attacks and defenses on graphs.



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1. Introduction

Graphs can be used as the denotation of a large number of systems across various areas such as social science (social networks), natural science (physical systems, and protein-protein interaction networks) and knowledge graphs. Graph Neural Networks (GNNs), which generalize traditional deep neural networks (DNNs) to graphs, pave a new way to effectively learn representations for graphs 

(Wu et al., 2019b). Due to their strong representation learning capability, GNNs have gained practical significance in various applications ranging from data mining (Kipf and Welling, 2016)

, natural language processing 

(Marcheggiani and Titov, 2017)

, and computer vision 

(Landrieu and Simonovsky, 2018) to healthcare and biology (Ma et al., 2018).

Figure 1. An example of adversarial attack on graph data. The goal of the GNN is to predict the color of the nodes. Here node 7 is the target node. Attacker aims to change the prediction of GNN on node 7 by modifying the edges and features.

As new generalizations of traditional DNNs to graphs, GNNs inherit both advantages and disadvantages of traditional DNNs. Similar to traditional DNNs, GNNs are also powerful in learning representations of graphs and have permeated numerous areas of science and technology. Traditional DNNs are easily fooled by adversarial attacks (Goodfellow et al., 2014; Xu et al., 2019a). In other words, the adversary can insert slight perturbation during either the training or test phases, and the DNN models will totally fail. It is evident  (Zügner et al., 2018) that GNNs also inherit this drawback. The attacker can generate graph adversarial perturbations by manipulating the graph structure or node features to fool the GNN models. As illustrated in Figure 1, originally node

was classified by the GNN model as a green node; after node

creates a new connection with node and modifies its own features, the GNN model misclassifies it as a blue node. Such vulnerability of GNNs has arisen tremendous concerns on applying them in safety-critical applications such as financial system and risk management. For example, in a credit scoring system, fraudsters can fake connections with several high-credit customers to evade the fraudster detection models; and spammers can easily create fake followers to increase the chance of fake news being recommended and spread. Therefore, there is an urgent need to investigate graph adversarial attacks and their countermeasures.

Pushing this research has a great potential to facilitate the successful adoption of GNNs in a broader range of fields, which encourages increasing attention on graph adversarial attacks and defenses in recent years. Thus, it is necessary and timely to provide a comprehensive and systematic overview on existing algorithms. Meanwhile, it is of great importance to deepen our understandings on graph adversarial attacks via empirical study. These understandings can not only provide knowledge about the behaviors of attacks but also offer insights for us to design defense strategies. These motivate this survey with the following key purposes:

  • We categorize existing attack methods from various perspectives in Section 3 and review representative algorithms in Section 4.

  • We classify existing countermeasures according to their defense strategies and give a review on representative algorithms for each category in Section 5.

  • We perform empirical studies based on the repository we developed that provide comprehensive understandings on graph attacks and defenses in Section 6.

  • We discuss some promising future directions in Section 7.

2. Preliminaries and Definitions

Before presenting the review and empirical studies, we first introduce concepts, notations and definitions in this section.

2.1. Learning on Graph Data

In this survey, we use to denote the structure of a graph where is the set of nodes and is the edge set. We use matrix to denote the adjacency matrix of , where each entry means nodes and are connected in . Furthermore, we use to denote the node attribute matrix where

is the dimension of the node feature vectors. Thus, graph data can be denoted as

. There are a lot of learning tasks on graphs and in this work, we focus on the classification problems on graphs. Furthermore, we use with parameters to denote the learning models in this survey.

Node-Level Classification For node-level classification, each node in the graph belongs to a class in the label set . The graph model aims to learn a neural network, based on labeled nodes (training nodes), denoted as , to predict the class of unlabeled nodes (test nodes). The training objective function can be formulated as:


where and are the predicted and the true label of node and

is a loss function such as cross entropy.

Graph-Level Classification For graph-level classification, each individual graph has a class in the label set . We use to denote a set of graphs, and is the labeled set (training set) of . The goal of graph-level classification is to learn a mapping function to predict the labels of unlabeled graphs. Similar to node-level classification, the objective function can be formulated as


where is the labeled graph with ground truth and is the prediction of the graph .

2.2. A General Form of Graph Adversarial Attack

Based on the objectives in Section 2.1, we can define a general form of the objective for adversarial attacks, which aims to maximize the loss value of the model in order to get wrong predictions. Thus, the problem of node-level graph adversarial attacks can be stated as:

Problem 1 ().

Given and victim nodes subset . Let denote the class for node (predicted or using ground truth). The goal of the attacker is to find a perturbed graph that maximizes the loss value of the victim nodes,


where can either be or . Note that is chosen from a constrained domain . Given a fixed perturbation budget , a typical can be implemented as,

Notations Description Notations Description
Graph Target node
Perturbed graph Label of node
The set of nodes Neural network model
The set of labeled nodes Loss function
The set of edges Pair-wise loss function
Adjacency matrix norm
Perturbed adjacency matrix Perturbation budget
Node attribute matrix

Predicted probability

Perturbed node attribute matrix Hidden representation of node
Dimension of node features Edge between node and
Table 1. Commonly used notations

We omit the definition of graph-level adversarial attacks since (1) the graph-level adversarial attacks can be defined similarly and (2) the majority of the adversarial attacks and defenses focus on node-level. Though adversarial attacks have been extensively studied in the image domain, we still need dedicated efforts for graphs due to unique challenges – (1) graph structure is discrete; (2) the nodes in the graph are not independent; and (3) it is difficult to measure whether the perturbation on the graph is imperceptible or not.

2.3. Notations

With the aforementioned definitions, we list all the notations which will be used in the following sections in Table 1.

3. Taxonomy of Graph Adversarial Attacks

In this section, we briefly introduce the main taxonomy of adversarial attacks on graph structured data. Attack algorithms can be categorized into different types based on different goals, resources, knowledge and capacity of attackers. We try to give a clear overview on the main components of graph adversarial attacks.

3.1. Attacker’s Capacity

The adversarial attacks can happen at two phases, i.e., the model training and model testing. It depends on the attacker’s capacity to insert adversarial perturbation:

  • Evasion Attack: Attacking happens after the GNN model is trained or in the test phase. The model is fixed, and the attacker cannot change the model parameter or structure. The attacker performs evasion attack when in Eq. (3).

  • Poisoning Attack: Attacking happens before the GNN model is trained. The attacker can add “poisons” into the model training data, letting trained model have malfunctions. It is the case when in Eq. (3).

3.2. Perturbation Type

The attacker can insert adversarial perturbations from different aspects. The perturbations can be categorized as modifying node features, adding/deleting edges, and adding fake nodes. Attackers should also keep the perturbation unnoticeable, otherwise it would be easily detected.

  • Modifying Feature: Attackers can slightly change the node features while maintaining the graph structure.

  • Adding or Deleting Edges: Attackers can add or delete edges under certain budget of total actions.

  • Injecting Nodes: Attackers can insert fake nodes to the graph, and link it with some benign nodes in the graph.

3.3. Attacker’s Goal

According to the goals of attacks, we can divide the attacks into the following two categories

  • Targeted Attack: There is a small set of test nodes. The attacker aims to let the trained model misclassify these test samples. It is the case when in Eq. (3). We can further divide targeted attacks into (1) direct attack where the attacker directly modifies the features or edges of the target nodes and (2) influencer attack where the attacker can only manipulate other nodes to influence the targets.

  • Untargeted Attack: The attacker aims to insert poisons to let the trained model have bad overall performance on all test data. It is the case when in Eq. (3).

3.4. Attacker’s Knowledge

Attacker’s knowledge means how much information an attacker knows about the model that he aims to attack. Usually, there are three settings:

  • White-box Attack: All information about the model parameters, training input (e.g, adjacency matrix and attribute matrix) and the labels are given to the attacker.

  • Gray-box Attack:

    The attacker only has limited knowledge about the victim model. For example, the attacker cannot access the model paramerters but can access the training labels. Then it can utilize the training data to train surrogate models to estimate the information from victim model.

  • Black-box Attack: The attacker does not have access to the model’s parameters or training labels. It can access the adjacency matrix and attribute matrix, and do black-box query for output scores or labels.

3.5. Victim Models

In this part we are going to summarize the victim models that have been proven to be susceptible to adversarial examples.

Graph Neural Networks Graph neural networks are powerful tools in learning representation of graphs (Sun et al., 2018). One of the most successful GNN variants is Graph Convolutional Networks (GCN) (Kipf and Welling, 2016). GCN learns the representation for each node by keeping aggregating and transforming the information from its neighbor nodes. Though GNNs can achieve high performance in various tasks, studies have demonstrated that GNNs including GCN are vulnerable to adversarial attacks (Zügner et al., 2018; Sun et al., 2018).

Other Graph Learning Algorithms In addition to graph neural networks, adversary may attack some other important algorithms for graphs such as network embeddings including LINE (Tang et al., 2015) and Deepwalk (Perozzi et al., 2014), graph-based semi-supervised learning (G-SSL) (Zhu and Ghahramani, 2002), and knowledge graph embedding  (Bordes et al., 2013; Lin et al., 2015).

Attack Methods
Targeted or
Evasion or
Perturbation Type Application Victim Model
PGD, Min-max (Xu et al., 2019b) White-box Untargeted Both Add/Delete edges Node Classification GNN
IG-FGSM (Wu et al., 2019a)
IG-JSMA (Wu et al., 2019a)
White-box Both Evasion
Add/Delete edges
Modify features
Node Classification GNN
(Wang and Gong, 2019)
Targeted Poisoning Add/Delete edges Node Classification GNN
Nettack (Zügner et al., 2018) Gray-box Targeted Both
Add/Delete edges
Modify features
Node Classification GNN
Metattack (Zügner and Günnemann, 2019a) Gray-box Untargeted Poisoning Add/Delete edges Node Classification GNN
NIPA (Sun et al., 2019) Gray-box Untargeted Poisoning Inject nodes Node Classification GNN
RL-S2V (Dai et al., 2018) Black-box Targeted Evasion Add/Delete edges
Graph Classification
Node Classification
ReWatt (Ma et al., 2019) Black-box Untargeted Evasion Add/Delete edges Graph Classification GNN
(Liu et al., 2019)
Untargted Poisoning
Flip label
Modify features
GF-Attack (Chang et al., 2019) Black-box Targeted Evasion Add/Delete edges Node Classification
(Bojchevski and Günnemann, 2018) Black-box Both Poisoning Add/Delete edges
Node Classification
Community Detection
(Zhang et al., 2019) White-box Targeted Poisoning Add/Delete facts Plausibility Prediction
CD-Attack (Li et al., 2020) Black-box Targeted Poisoning Add/Delete edges Community Detection
Table 2. Categorization of representative attack methods

4. Graph Adversarial Attacks

In this section, we review representative algorithms for graph adversarial attacks. Following the categorizations in the previous section, we first divide these algorithms into white-box, gray-box and black-box and then for algorithms in each category, we further group them into targeted and untargeted attacks. An overall categorization of representative attack methods is shown in Table 2

. In addition, some open source implementations of representative algorithms are listed in Table 


4.1. White-box Attacks

In white-box attack setting, the adversary has access to any information about the victim model such as model parameters, training data, labels, and predictions. Although in most of the real world cases we do not have the access to such information, we can still assess the vulnerability of the victim models under the worst situation. Typically, white-box attacks use the gradient information from the victim model to guide the generation of attacks (Chen et al., 2018b; Xu et al., 2019b; Wu et al., 2019a; Chen et al., 2019a).

4.1.1. Targeted Attack

Targeted attack aims to mislead the victim model to make wrong predictions on some target samples. A lot of studies follow the white-box targeted attack setting with a wide range of real-world applications. FGA (Chen et al., 2018b)

extracts the link gradient information from GCN, and then greedily selects the pair of nodes with maximum absolute gradient to modify the graph iteratively. Genetic algorithm based Q-Attack is proposed to attack a number of community detection algorithms 

(Chen et al., 2019a). Iterative gradient attack (IGA) based on the gradient information in the trained graph auto-encoder, which is introduced to attack link prediction (Chen et al., 2018a). Furthermore, the vulnerability of knowledge graph embedding is investigated in  (Zhang et al., 2019) and the plausibility of arbitrary facts in knowledge graph can be effectively manipulated by the attacker. Recommender systems based on GNNs are also vulnerable to adversarial attacks, which is shown in (Zhou et al., 2020). In addition, there are great efforts on attacking node classification. Traditional attacks in the image domain always use models’ gradients to find adversarial examples. However, due to the discrete property of graph data, directly calculating gradients of models could fail. To solve this issue, the work (Wu et al., 2019a) suggests to use integrated gradient (Sundararajan et al., 2017) to better search for adversarial edges and feature perturbations. During the attacking process, the attacker iteratively chooses the edge or feature which has the strongest effect to the adversarial objective. By this way, it can cause the victim model to misclassify target nodes with a higher successful rate. The work (Zang et al., 2020) assumes there is a set of “bad actor” nodes in a graph. When they flip the edges with any target nodes in a graph, it will cause the GNN model to have a wrong prediction on the target node. These “bad actor” nodes are critical to the safety of GNN models. For example, Wikipedia has hoax articles which have few and random connections to real articles. Manipulating the connections of these hoax articles will cause the system to make wrong prediction of the categories of real articles.

4.1.2. Untargeted Attack

Currently there are not many studies on untargeted white-box attack, and topology attack (Xu et al., 2019b) is one representative algorithm. It first constructs a binary symmetric perturbation matrix where indicates to flip the edge between and and means no modification on . Thus, the goal of the attacker is to find that minimizes the predefined attack loss given a finite budget of edge perturbations , i.e., . It considers two different attack scenarios: attacking pre-trained GNN with fixed parameters and attacking a re-trainable GNN . For attacking a fixed , the problem can be formulated as,


It utilizes the Projected Gradient Descent (PGD) algorithm in (Madry et al., 2017) to search the optimal . Note that the work (Madry et al., 2017) is also one popular attack algorithm in the image domain. For the re-trainable GNNs, parameter will be retrained after adversarial manipulation, thus the attack problem is formulated as a min-max form where the inner maximization can be solved by gradient ascent and the outer minimization can be solved by PGD.

4.2. Gray-box Attacks

White-box attacks assume that attackers can calculate gradient through model parameters, which is not always practical in real-world scenarios. Gray-box attacks are proposed to generate attacks with limited knowledge on the victim model (Zügner et al., 2018; Zügner and Günnemann, 2019a; Sun et al., 2019). Usually they first train a surrogate model with the labeled training data to approximate the information of the victim model and then generate perturbations to attack the surrogate model. It is noted that these models need the access to the labels of training data, thus they are not black-box attacks that will be introduced in the following subsection.

4.2.1. Targeted Attack

The early work on targeted gray-box attacks is for graph clustering (Chen et al., 2017). It demonstrates that injecting noise to a DNS query graph can degrade the performance of graph embedding models. Different from  (Chen et al., 2017), the work (Zügner et al., 2018) proposes an attack method called Nettack to generate structure and feature attacks, aiming at solving Eq. (3). Besides, they argue that only limiting the perturbation budgets cannot always make the perturbation “unnoticeable”. They suggest the perturbed graphs should also maintain important graph properties, including degree distribution and feature co-occurrence. Therefore, Nettack first selects possible perturbation candidates not violating degree distribution and feature co-occurrence of the original graph. Then it greedily chooses the perturbation that has the largest score to modify the graph, where the score is defined as,


where is the probability of node to be the class predicted by the surrogate model. Thus, the goal of the attacker is to maximize the difference in the log-probabilities of the target node . By doing this repeatedly until reaching the perturbation budge , it can get the final modified graph. Furthermore, it suggests that such graph attack can also transfer from model to model, just as the attacks in the image domain (Goodfellow et al., 2014). The authors also conduct influencer attacks where they can only manipulate the nodes except the target. It turns out that influencer attacks lead to a lower decrease in performance compared with directly modifying target node given the same perturbation budget.

4.2.2. Untargeted Attack

Although following the same way of training a surrogate model as Nettack, Metattack (Zügner and Günnemann, 2019a) is a kind of untargeted poisoning attack. It tackles the bi-level problem in Eq. (3) by using meta-gradient. Basically, it treats the graph structure matrix as a hyper-parameter and the gradient of the attacker loss with respect to it can be obtained by:


Note that is actually a function with respect to both and . If is obtained by some differential operations, we can compute as follows,


where is often obtained by gradient descent in fixed iterations . At iteration , the gradient of with respect to can be formulated as,


where denotes learning rate of the gradient descent operation. By unrolling the training procedure from back to , we can get and then . A greedy approach is applied to select the perturbation based on the meta gradient.

Instead of modifying the connectivity of existing nodes, a novel reinforment learning method for node injection poisoning attacks (NIPA) (Sun et al., 2019) is proposed to inject fake nodes into graph data . Specifically, NIPA first injects singleton nodes into the original graph. Then in each action , the attacker first chooses an injected node to connect with another node in the graph and then assigns a label to the injected node. By doing this sequentially, the final graph is statistically similar to the original graph but can degrade the overall model performance.

4.3. Black-box Attacks

Different from gray-box attacks, black-box attacks (Dai et al., 2018; Ma et al., 2019; Sun et al., 2019; Bojchevski and Günnemann, 2018; Chang et al., 2019) are more challenging since the attacker can only access the input and output of the victim model. The access of parameters, labels and predicted probability is prohibited.

4.3.1. Targeted Attack

As mentioned earlier, training a surrogate model requires access to the labels of training data, which is not always practical. We hope to find a way that we only need to do black-box query on the victim model (Dai et al., 2018) or attack the victim in an unsupervised fashion (Bojchevski and Günnemann, 2018; Chang et al., 2019).

To do black-box query on the victim model, reinforcement learning is introduced. RL-S2V 

(Dai et al., 2018)

is the first work to employ reinforcement learning technique to generate adversarial attacks on graph data under the black-box setting. They model the attack procedure as a Markov Decision Process (MDP) and the attacker is allowed to modify

edges to change the predicted label of the target node . They study both node-level (targeted) and graph-level (untargeted) attacks. For node-level attack, they define the MDP as follows,

  • State The state is represented by the tuple where is the modified graph at time step .

  • Action A single action at time step is denoted as . For each action , the attacker can choose to add or remove an edge from the graph. Furthermore, a hierarchical structure is applied to decompose the action space.

  • Reward Since the goal of the attacker is to change the classification result of the target node , RL-S2V gives non-zero reward to the attacker at the end of the MDP:

    In the intermediate steps, the attacker receives no reward, i.e., .

  • Termination The process terminates when the attacker finishes modifying edges.

Since they define the MDP of graph-level attack in the similar way, we omit the details. Further, the Q-learning algorithm (Mnih et al., 2013) is adopted to solve the MDP and guide the attacker to modify the graph.

Instead of attacking node classification, the work (Bojchevski and Günnemann, 2018) shows a way to attack the family of node embedding models in the black-box setting. Inspired by the observation that DeepWalk can be formulated in matrix factorization form (Qiu et al., 2018), they maximize the unsupervised DeepWalk loss with matrix perturbation theory by performing edge flips. It is further demonstrated that the perturbed structure is transferable to other models like GCN and Label Propagation. However, this method only considers the structure information. GF-Attack (Chang et al., 2019) is proposed to incorporate the feature information into the attack model. Specifically, they formulate the connection between the graph embedding method and general graph signal process with graph filter and construct the attacker based on the graph filter and attribute matrix. GF-Attack can also be transferred to other network embedding models and achieves better performance than the method in  (Bojchevski and Günnemann, 2018).

4.3.2. Untargeted Attack

It is argued that the perturbation constraining only the number of modified edges may not be unnoticeable enough. A novel framework ReWatt (Ma et al., 2019) is proposed to solve this problem and perform untargeted graph-level attack. Still employing a reinforcement learning framework, ReWatt adopts the rewiring operation instead of simply adding/deleting an edge in one single modification to make perturbation more unnoticeable. One rewiring operation involves three nodes and , where ReWatt removes the existing edge between and and connects and . ReWatt also constrains to be the 2-hop neighbor of to make perturbation smaller. Such rewiring operation does not change the number of nodes and edges in the graph and it is further proved that such rewiring operation affects algebraic connectivity and effective graph resistance, both of which are important graph properties based on graph Laplacian, in a smaller way than adding/deleting edges.

5. Countermeasures Against Graph Adversarial Attacks

In previous sections, we have shown that graph neural networks can be easily fooled by unnoticeable perturbation on graph data. The vulnerability of graph neural networks poses great challenges to apply them in safety-critical applications. In order to defend the graph neural networks against these attacks, different countermeasure strategies have been proposed. The existing methods can be categorized into the following types: (1) adversarial training, (2) adversarial perturbation detection, (3) certifiable robustness, (4) graph purification, and (5) attention mechanism.

5.1. Adversarial Training

Adversarial training is a widely used countermeasure for adversarial attacks in image data (Goodfellow et al., 2014). The main idea of adversarial training is to inject adversarial examples into the training set such that the trained model can correctly classify the future adversarial examples. Similarly, we can also adopt this strategy to defend graph adversarial attacks as follows,


where , denote the perturbation on , respectively; and stand for the domains of imperceptible perturbation. The min-max optimization problem in Eq (10) indicates that adversarial training involves two process: (1) generating perturbations that maximize the prediction loss and (2) updating model parameters that minimize the prediction loss. By alternating the above two process iteratively, we can train a robust model against to adversarial attacks. Since there are two inputs, i.e., adjacency matrix and attribute matrix , adversarial training can be done on them separately. To generate perturbations on the adjacency matrix, it is proposed to randomly drop edges during adversarial training (Dai et al., 2018). Though such simple strategy cannot lead to very significant improvement in classification accuracy (1% increase), it shows some effectiveness with such cheap adversarial training. Furthermore, projected gradient descent is used to generate perturbations on the discrete input structure, instead of randomly dropping edges (Xu et al., 2019b). On the other hand, an adversarial training strategy with dynamic regularization is proposed to perturb the input features (Feng et al., 2019). Specifically, it includes the divergence between the prediction of the target example and its connected examples into the objective of adversarial training, aiming to attack and reconstruct graph smoothness. Furthermore, batch virtual adversarial training (Deng et al., 2019) is proposed to promote the smoothness of GNNs and make GNNs more robust against adversarial perturbations. Several other variants of adversarial training on the input layer are introduced in (Chen et al., 2019b; Dai et al., 2019; Wang et al., 2019).

The aforementioned adversarial training strategies face two main shortcomings: (1) they generate perturbations on and separately; and (2) it is not easy to perturb the graph structure due to its discreteness. To overcome the shortcomings, instead of generating perturbation on the input, a latent adversarial training method injects perturbations on the first hidden layer (Jin and Zhang, 2019):


where denotes the representation matrix of the first hidden layer and is some perturbation on . It is noted that the hidden representation is continuous and it incorporates the information from both graph structure and node attributes.

5.2. Detecting Adversarial Perturbations

To resist graph adversarial attacks during the test phase, there is one main strategy called adversary detection. These detection models protect the GNN models by exploring the intrinsic difference between adversarial edges/nodes and the clean edges/nodes (Xu et al., 2018; Ioannidis et al., 2019). The work (Xu et al., 2018)

is the first work to propose detection approaches to find adversarial examples on graph data. It introduces four methods to distinguish adversarial edges or nodes from the clean ones including (1) link prediction (2) sub-graph link prediction (3) graph generation models and (4) outlier detection. These methods have shown some help to correctly detect adversarial perturbations. The work 

(Ioannidis et al., 2019) introduces a method to randomly draw subsets of nodes, and relies on graph-aware criteria to judiciously filter out contaminated nodes and edges before employing a semi-supervised learning (SSL) module. The proposed model can be used to detect different anomaly generation models, as well as adversarial attacks.

5.3. Certifiable Robustness

Previous introduced adversarial training strategies are heuristic and only show experimental benefits. However, we still do not know whether there exist adversarial examples even when current attacks fail. Therefore, there are works 

(Zügner and Günnemann, 2019b; Bojchevski and Günnemann, 2019; Jia et al., 2020) considering to seriously reason the safety of graph neural networks which try to certify the GNN’s robustness. As we know, GNN’s prediction on one node always depends on its neighbor nodes. In (Zügner and Günnemann, 2019b), they ask the question: which nodes in a graph are safe under the risk of any admissible perturbations of its neighboring nodes’ attributes. To answer this question, for each node and its corresponding label , they try to find an upper bound of the maximized margin loss:


where denotes the set of all allowed attributes perturbations. This upper bound is called the certificate of node , and it is tractable to calculate. Therefore, for , if , any attribute perturbation in can not change the model’s prediction, because its maximized margin loss is below 0. During the test phase, they calculate the certificate for all test nodes, thus they can know how many nodes in a graph is absolutely safe under attributes perturbation. Moreover, this certificate is trainable, directly minimizing the certificates will help more nodes become safe. However, the work (Zügner and Günnemann, 2019b) only considers the perturbations on node attributes. Analyzing certifiable robustness from a different perspective, the work (Bojchevski and Günnemann, 2019) deals with the case when the attacker only manipulates the graph structure. It derives the robustness certificates (similar to Eq. (12)) as a linear function of personalized PageRank (Jeh and Widom, 2003), which makes the optimization tractable. Besides the works concentrate on GNN node classification tasks, there are also other works studying certifiable robustness on GNN’s other applications such as community detection (Jia et al., 2020).

5.4. Graph Purification

Both adversarial training or certifiable defense methods only target on resisting evasion attacks, which means that the attack happens during the test time. While, graph purification defense methods mainly focus on defending poisoning attacks. Since the poisoning attacks insert poisons into the training graph, purification methods first purify the perturbed graph data and then train the GNN model on the purified graph. By this way, the GNN model is trained on a clean graph. The work (Wu et al., 2019a) proposes a purification method based on two empirical observations of the attack methods: (1) Attackers usually prefer adding edges over removing edges or modifying features and (2) Attackers tend to connect dissimilar nodes. As a result, they propose a defense method by eliminating the edges whose two end nodes have small Jaccard Similarity (Said et al., ). Because these two nodes are different and it is not likely they are connected in reality, the edge between them may be adversarial. The experimental results demonstrate the effectiveness and efficiency of the proposed defense method. However, this method can only work when the node features are available. In (Entezari et al., 2020), it is observed that Nettack (Zügner et al., 2018)

generates the perturbations which mainly changes the small singular values of the graph adjacency matrix. Thus it proposes to purify the perturbed adjacency matrix by using truncated SVD to get its low-rank approximation. it further shows that only keeping the top

singular values of the adjacency matrix is able to defend Nettack and improve the performance of GNNs.

5.5. Attention Mechanism

Different from the purification methods which try to exclude adversarial perturbations, attention-based defense methods aim to train a robust GNN model by penalizing model’s weights on adversarial edges or nodes. Basically, these methods learn an attention mechanism to distinguish adversarial edges and nodes from the clean ones, and then make the adversarial perturbations contribute less to the aggregation process of the GNN training. The work (Zhu et al., 2019) first assumes that adversarial nodes may have high prediction uncertainty, since adversary tends to connect the node with nodes from other communities. In order to penalize the influence from these uncertain nodes, they propose to model the -th layer hidden representation

of nodes as Gaussian distribution with mean value

and variance



where the uncertainty can be reflected in the variance . When aggregating the information from neighbor nodes, it applies an attention mechanism to penalize the nodes with high variance,


where is the attention score assigned to node and is a hyper-parameter. Furthermore, it is verified that the attacked nodes do have higher variances than normal nodes and the proposed attention mechanism does help mitigate the impact brought by adversarial attacks.

The work in (Tang et al., 2020) suggests that to improve the robustness of one target GNN model, it is beneficial to include the information from other clean graphs, which share the similar topological distributions and node attributes with the target graph. For example, Facebook and Twitter have social network graph data that share similar domains; Yelp and Foursquare have similar co-review graph data. Thus, it first generates adversarial edges on the clean graphs, which serve as the supervision of known perturbation. With this supervision knowledge, it further designs the following loss function to reduce the attention score of adversarial edges:


where denotes the expectation, represents normal edges in the graph, is the attention score assigned to edge and is a hyper parameter controlling the margin between the expectation of two distributions. It then adopts meta-optimization to train a model initialization and fine-tunes it on the target poisoned graph to get a robust GNN model.

6. Empirical Study

We have developed a repository that includes the majority of the representative attack and defense algorithms on graphs222 The repository enables us to deepen our understandings on graph attacks and defends via empirical study. Next we first introduce the experimental settings and then present the empirical results and findings.

6.1. Experimental Setup

Different attack and defense methods have been designed under different settings. Due to the page limitation, we perform the experiments with one of the most popular settings – the untargeted poisoning setting. Correspondingly we choose representative attack and defense methods that have been designed for this setting. Three representative attack methods are adopted to generate perturbations including DICE (Waniek et al., 2018), Metattack (Zügner and Günnemann, 2019a) and Topology attack (Xu et al., 2019b). It is noted that DICE is a white-box attack which randomly connects nodes with different labels or drops edges between nodes sharing the same label. To evaluate the performance of different defense methods under adversarial attacks, we compare the robustness of the natural trained GCN (Kipf and Welling, 2016) and four defense methods on those attacked graphs, i.e., GCN (Kipf and Welling, 2016), GCN-Jaccard (Wu et al., 2019a), GCN-SVD (Entezari et al., 2020), RGCN (Zhu et al., 2019) and GAT (Veličković et al., 2017). Following (Zügner and Günnemann, 2019a), we use three datasets: Cora, Citeseer (Sen et al., 2008) and Polblogs (Adamic and Glance, 2005). For each dataset, we randomly choose 10% of nodes for training, 10% of nodes for validation and the remaining 80% for test. We repeat each experiment for 5 times and report the average performance. On Cora and Citeseer datasets, the most destructive variant CE-min-max (Xu et al., 2019b) is adopted to implement Topology attack. But CE-min-max cannot converge on Polblogs dataset, we adopt another variant called CE-PGD (Xu et al., 2019b) on this dataset.

6.2. Analysis on Attacked Graph

One way to understand the behaviors of attacking methods is to compare the properties of the clean graph and the attacked graph. In this subsection, we perform this analysis from both global and local perspectives. Global Measure We have collected five global properties from both clean graphs and perturbed graphs generated by the three attacks on the three datasets. These properties include the number of added edges, the number of deleted edges, the number of edges, the rank of the adjacent matrix, and clustering coefficient. We only show the results of Metattack in Table 3. Results for DICE and Topology attacks can be found in Appendix A. Note that we vary the perturbations from to with a step of and perturbation denotes the original clean graph. It can be observed from the table:

  • Attackers favor adding edges over deleting edges.

  • Attacks are likely to increase the rank of the adjacency matrix.

  • Attacks are likely to reduce the connectivity of a graph. The clustering coefficients of a perturbed graph decrease with the increase of the perturbation rate.

Dataset (%) edge+ edge- edges ranks
Cora 0 0 0 5069 2192 0.2376
5 226 27 5268 2263 0.2228
10 408 98 5380 2278 0.2132
15 604 156 5518 2300 0.2071
20 788 245 5633 2305 0.1983
25 981 287 5763 2321 0.1943
Citeseer 0 0 0 3668 1778 0.1711
5 181 2 3847 1850 0.1616
1 341 25 3985 1874 0.1565
15 485 65 4089 1890 0.1523
20 614 119 4164 1902 0.1483
25 743 174 4236 1888 0.1467
Polblogs 0 0 0 16714 1060 0.3203
5 732 103 17343 1133 0.2719
10 1347 324 17737 1170 0.2825
15 1915 592 18038 1193 0.2851
20 2304 1038 17980 1193 0.2877
25 2500 1678 17536 1197 0.2723
Table 3. Properties of attacked graphs under Metattack. Note that denotes perturbation rate and perturbation indicates the original clean graph.

Local Measure We have also studied two local properties including the feature similarity and label equality between two nodes connected by three kinds of edges: the newly added edges, the deleted edges and the normal edges which have not been changed by the attack methods. Since features are binary in our datasets, we use jaccard similarity as the measure for feature similarity. For label equality, we report the ratio if two nodes share the same label or have different labels. The feature similarity and label equality results are demonstrated in Figures 2 and 3, respectively. We show the results for Metattack with perturbations. Results for DICE and Topology attacks can be found in Appendix B. Note that we do not have feature similarity results on Polblogs since this dataset does not have node features. We can make the following observations from the figures.

  • Attackers tend to connect nodes with different labels and dissimilar features.

  • Attackers tend to remove edges from nodes which share similar features and same label.

(a) Cora
(b) Citeseer
Figure 2. Node feature similarity for Metattack
(a) Cora
(b) Citeseer
(c) Polblogs
Figure 3. Label equality for Metattack

6.3. Attack and Defense Performance

In this subsection, we study how the attack methods perform and whether the defense methods can help resist to attacks. Similarly, we vary the perturbations from to with a step of . The results are demonstrated in Table 4. We show the performance for Metattack. Results for DICE and Topology attacks are shown in Appendix C. Note that we do not have the performance for Jaccard defense model in Polblogs since this mode requires node features and Polblogs does not provide node features. According to the results, we have the following observations:

  • With the increase of the perturbations, the performance of GCN dramatically deceases. This result suggests that Metattack can lead to a significant reduce of accuracy on the GCN model.

  • When the perturbations are small, we observe small performance reduction for defense methods which suggests their effectiveness. However, when the graphs are heavily poisoned, their performance also reduces significantly which indicates that efforts are needed to defend heavily poisoning attacks.

Dataset (%) 0 5 10 15 20 25
Cora GCN 83.10 76.69 65.58 54.88 48.66 38.44

Jaccard111 82.39 81.02 77.28 72.74 69.16 64.56

SVD222 77.97 75.67 70.51 64.34 55.89 45.92

RGCN 84.81 81.32 72.12 60.25 49.75 37.76

GAT 81.69 74.75 61.69 52.56 45.30 38.52

GCN 74.53 72.59 63.96 61.66 50.58 44.32

Jaccard111 74.82 73.60 73.50 72.80 72.97 72.53

SVD222 70.32 71.30 67.58 63.86 56.91 45.28

RGCN 74.41 72.68 71.15 69.38 67.93 67.24

GAT 74.23 72.01 67.12 57.70 47.97 38.70

GCN 95.80 73.93 72.07 67.69 62.29 52.97

SVD222 94.99 82.64 71.27 66.09 61.37 52.82

RGCN 95.60 72.01 67.12 57.70 47.97 38.70

GAT 95.40 84.83 77.03 69.94 53.62 53.76
  • Jaccard: GCN-Jaccard defense model.

  • SVD: GCN-SVD defense model.

Table 4. Performance(Accuracy) under Metattack

7. Conclusion and Future Directions

In this survey, we give a comprehensive overview of an emerging research field, adversarial attacks and defenses on graph data. We investigate the taxonomy of graph adversarial attacks, and review representative adversarial attacks and the corresponding countermeasures. Furthermore, we conduct empirical study to show how different defense methods behave under different attacks, as well as the changes in important graph properties by the attacks. Via this comprehensive study, we have gained deep understandings on this area that enables us to discuss some promising research directions.

  • Imperceptible perturbation measure. Different from image data, humans cannot easily tell whether a perturbation on graph is imperceptible or not. The norm constraint on perturbation is definitely not enough. Currently only very few existing work study this problem, thus finding concise perturbation evaluation measure is of great urgency.

  • Different graph data. Existing works mainly focus on static graphs with node attributes. Complex graphs such as graphs with edge attributes and dynamic graphs are not well-studied yet.

  • Existence and transferability of graph adversarial examples. There are only a few works discussing about the existence and transferability of graph adversarial examples. Studying this topic is important for us to understand our graph learning algorithm, thus helping us build robust models.

  • Graph structure learning. By analyzing the attacked graph, we find that attacks are likely to change certain properties of graphs. Therefore, we can learn a graph from the poisoned graphs by exploring these properties to build robust GNNs.


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Appendix A Appendix

a.1. Open Source Code

Methods Framework Github Link
Attack PGD, Min-max (Xu et al., 2019b)
(l)2-4 DICE (Waniek et al., 2018) python
(l)2-4 Nettack (Zügner et al., 2018) tensorflow
(l)2-4 Metattack (Zügner and Günnemann, 2019a)
(l)2-4 RL-S2V (Dai et al., 2018) pytorch
(l)2-4 (Bojchevski and Günnemann, 2018) tensorflow
(l)2-4 GF-Attack (Chang et al., 2019) tensoflow
Defense RGCN (Zhu et al., 2019)
(l)2-4 GCN-Jaccard (Wu et al., 2019a) pytorch
(l)2-4 GCN-SVD (Entezari et al., 2020) pytorch
Training (Xu et al., 2019b)
(l)2-4 PA-GNN (Tang et al., 2020) tensorflow
(l)2-4 Graph-Cert (Bojchevski and Günnemann, 2019) python
Table 5. A Summary of Open-source Implementations

a.2. Global Measures for Dice and Topology Attacks

Dataset (%) edges+ edges- edges ranks
Cora 0 0 0 5069 2192 0.2376
5 255 0 5324 2292 0.2308
10 508 0 5577 2369 0.2185
15 762 0 5831 2417 0.2029
20 1015 0 6084 2442 0.1875
25 1269 0 6338 2456 0.1736
Citeseer 0 0 0 3668 1778 0.1711
5 185 0 3853 1914 0.1666
10 368 0 4036 2003 0.1568
15 552 0 4220 2058 0.1429
20 735 0 4403 2077 0.1306
25 918 0 4586 2087 0.1188
Polblogs 0 0 0 16714 1060 0.3203
5 716 96 17334 1213 0.2659
10 1532 128 18118 1220 0.2513
15 2320 146 18887 1221 0.2408
20 3149 155 19708 1221 0.2317
25 3958 163 20509 1221 0.2238
Table 6. Properties of attacked graphs under Topology Attack
Dataset (%) edge+ edge- edges ranks
Cora 0 0 0 5069 2192 0.2376
5 125 128 5066 2210 0.2163
10 251 255 5065 2238 0.1966
15 377 383 5063 2246 0.1786
20 504 509 5063 2261 0.1583
25 625 642 5053 2270 0.1448
Citeseer 0 0 0 3668 1778 0.1711
5 91 92 3667 1803 0.1576
10 183 183 3668 1828 0.1408
15 276 274 3670 1840 0.1288
20 368 365 3672 1860 0.1187
25 462 455 36755 1871 0.1084
Polblogs 0 0 0 16714 1060 0.3203
5 420 415 16719 1155 0.2822
10 846 825 16736 1192 0.2487
15 1273 1234 16752 1208 0.2224
20 1690 1652 16752 1214 0.2009
25 2114 2064 16765 1217 0.1821
Table 7. Properties of attacked graphs under DICE Attack

a.3. Local Measures for Dice and Topology Attacks

(a) Cora
(b) Citeseer
Figure 4. Node feature similarity for Topology Attack
(a) Cora
(b) Citeseer
Figure 5. Node feature similarity for DICE Attack
(a) Cora
(b) Citeseer
(c) Polblogs
Figure 6. Label equality for Topology Attack
(a) Cora
(b) Citeseer
(c) Polblogs
Figure 7. Label equality for DICE Attack

a.4. Attack and Defense Performance for Dice and Topology Attacks

Dataset (%) 0 5 10 15 20 25
Cora GCN 83.10 82.20 81.15 80.54 79.40 77.78
Jaccard111 82.39 81.66 80.94 80.24 79.41 78.31
SVD222 77.97 76.55 74.35 72.71 59.77 70.41
RGCN 84.81 83.87 82.72 81.64 80.77 79.53
GAT 81.69 79.33 77.36 75.23 73.78 72.05
Citeseer GCN 74.53 74.21 73.90 72.36 72.27 71.50
Jaccard111 74.82 74.56 74.14 73.51 73.22 72.22
SVD222 70.32 70.91 70.27 69.19 67.63 66.82
RGCN 74.41 74.72 74.22 73.42 72.71 72.16
GAT 74.23 73.78 72.86 71.48 70.25 69.68
Polblogs GCN 95.80 92.78 90.78 90.12 88.28 87.79
SVD222 94.99 93.09 92.39 91.31 90.72 90.61
RGCN 95.60 92.72 90.70 89.80 88.34 87.28
GAT 95.40 93.56 91.82 91.27 89.65 89.30
  • Jaccard: GCN-Jaccard defense model.

  • SVD: GCN-SVD defense model.

Table 8. Performance(Accuracy) under DICE Attack
Dataset (%) 0 5 10 15 20 25
Cora GCN 83.10 71.82 68.96 66.77 64.21 62.52
Jaccard111 82.39 73.05 72.62 71.84 71.41 70.85
SVD222 77.97 78.17 75.92 73.69 72.03 70.11
RGCN 84.81 72.68 71.15 69.38 67.92 67.23
GAT 81.69 71.03 68.80 65.66 64.29 62.58
Citeseer GCN 74.53 79.29 75.47 72.89 70.12 68.49
Jaccard111 74.82 79.07 76.76 74.29 71.87 69.55
SVD222 70.32 78.17 75.92 73.69 72.03 70.11
RGCN 74.41 78.13 75.93 73.93 72.32 70.60
GAT 74.23 77.52 74.09 71.90 69.62 66.99
Polblogs GCN 95.80 72.04 65.87 63.35 61.06 58.49
SVD222 94.99 71.90 65.42 63.01 60.74 58.26
RGCN 95.60 71.27 65.30 62.76 60.25 57.89
GAT 95.40 72.56 65.97 63.35 60.94 58.77
  • Jaccard: GCN-Jaccard defense model.

  • SVD: GCN-SVD defense model.

Table 9. Performance(Accuracy) under Topology Attack