LGGAN generates labeled graphs from graph data.
problem Training generative models for graph-structured data with labels.
method LGGAN, a GAN approach, trains deep models for graph data with node labels.
result LGGAN generates diverse labeled graphs that match training data and outperforms alternatives.
Paper provides robustness bounds for GNNs against adversarial attacks.
problem Adversarial robustness of GNNs for graph-related tasks.
method PAC-Bayesian framework applied to GCN and MP-GNN.
result Spectral norms of diffusion matrix and weights govern robustness.
Pro-GNN defends graph neural networks from adversarial attacks by learning graph structure.
problem Vulnerability of GNNs to adversarial attacks on real-world graphs.
method Pro-GNN learns a structural graph and a robust GNN model jointly from perturbed graphs guided by intrinsic graph properties.
result Pro-GNN achieves significantly better performance than state-of-the-art defense methods, even on heavily perturbed graphs.
Generative Adversarial Graph Neural Network (Sig-Graph GAN) models financial time series data.
problem Challenges in generating synthetic data for non-stationary financial time series.
method Integrates time-series signature, LSTM, and GNNs with visibility graph algorithm.
result Sig-Graph GAN outperforms baseline methods in replicating time series data distributions.
Paper shows how to hide individuals in graphs to fool community detection models.
problem Adversarial attack on community detection models by hiding individuals.
method Iterative learning framework that updates a graph generator and a community detection model.
result Adversarial graphs generated by the method can fool multiple community detection models.
This paper explores vulnerabilities in hierarchical graph pooling neural networks for graph classification.
problem Vulnerability of hierarchical graph pooling neural networks in graph classification tasks.
method Proposes an adversarial attack framework using a surrogate model to generate adversarial samples.
result Adversarial samples can fool hierarchical GNN-based graph classification models, demonstrating their vulnerability.
The paper examines how neural network topology affects adversarial robustness.
problem Understanding how neural network topology influences adversarial robustness.
method Investigated the graph of input traversing all layers of a neural network, comparing clean and adversarial inputs.
result Under-optimized edges in neural network graphs are a source of adversarial vulnerability and can be used to detect adversarial inputs.
DefenseVGAE defends graph neural networks against adversarial attacks.
problem Vulnerability of GNNs to adversarial structural perturbations.
method Variational Graph Autoencoder (VGAE) to reconstruct graph structure.
result DefenseVGAE reduces adversarial perturbations and boosts GCN performance.
MMGAN creates graphs with higher-order motifs for better network simulation.
problem Generative models fail to capture higher-order connectivity patterns in real-world networks.
method Combines multiple biased random walks to capture different motif structures.
result Outperforms NetGAN at creating graphs with accurate network motif statistics.
Indirect attacks can fool graph classifiers even with poisoned neighbors.
problem How to evaluate and defend graph convolutional neural networks against indirect adversarial attacks.
method Proposed a method to generate adversarial perturbations on a single node far from the target.
result 99% attack success rate within two-hops from the target in two datasets.
Graph Information Bottleneck (GIB) optimizes graph representations for robustness against adversarial attacks.
problem Challenges in learning graph representations due to structure and feature information.
method GIB is an information-theoretic principle that balances expressiveness and robustness by maximizing mutual information between representation and target, while constraining mutual information with input data.
result GIB-based models are more robust to adversarial attacks, achieving up to 31% improvement.
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
problem Vulnerability of graph neural networks to adversarial attacks.
method Proposes AdvImmune, an algorithm that optimizes with meta-gradient to improve certifiable robustness.
result Remarkably improves the ratio of robust nodes by 12%, 42%, 65% with an affordable immune budget of only 5% edges.
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, training requires solving an expensive graph isomorphism problem, which previous approaches do not address or solve only approximately. In this wor…
Framework improves GCNs for graphless and adversarial settings.
problem Improving GCNs without graph data and making them robust to adversarial attacks.
method Joint probabilistic model with variational inference and Concrete distributions.
result Framework outperforms state-of-the-art algorithms on semi-supervised classification.
DiagNet uses adversarial learning and signed graph regularization for better mammography diagnosis.
problem Inadequate data and similarity between benign and cancerous masses in mammography.
method Adversarial learning to generate positive and negative mammograms, signed similarity graph, deep convolutional neural network training.
result DiagNet outperforms state-of-the-art in breast mass diagnosis.
We outline a detection method for adversarial inputs to deep neural networks. By viewing neural network computations as graphs upon which information flows from input space to out- put distribution, we compare the differences in graphs induced by different inputs. Specifically, by applying persistent homology to these …
Generative model for inferring graph from time series data.
problem Generating graphs conditioned on multivariate time series data.
method Time Series Conditioned Graph Generation-Generative Adversarial Networks (TSGG-GAN).
result Demonstrates effectiveness and generalizability of TSGG-GAN on synthetic and real-world datasets.
Bayesian optimisation method targets graph classification models against adversarial attacks.
problem Adversarial attacks on graph classification models, especially for graph-level tasks.
method Bayesian optimisation-based attack method for graph classification models.
result Effectiveness and flexibility of the proposed method validated on various graph classification tasks.
Graph neural networks are vulnerable to adversarial attacks by manipulating graph structure.
problem Vulnerability of Graph Neural Networks to adversarial attacks.
method Categorization and review of existing attacks and defenses.
result Developed a repository for empirical studies on graph adversarial attacks and defenses.
Paper improves robustness of GNNs against adversarial attacks.
problem Understanding robust generalization of GNNs in adversarial settings.
method Develops a sensitivity-aware PAC-Bayesian framework for MPGNNs.
result Derives tighter robust generalization bounds for MPGNNs.
Paper presents an optimization-based attack and defense for graph neural networks.
problem Adversarial robustness of graph neural networks (GNNs).
method Gradient-based attack and optimization-based adversarial training.
result Optimization-based attack can significantly decrease GNN classification performance with minimal edge perturbations.
We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs). BVAT addresses the shortcoming of GCNs that do not consider the smoothness of the model's output distribution against local perturbations around the input. We propose two algorithms, sample-based…
Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (\eg articles with citation link tend to be in the same class), graph neural networks could be more sensiti…
Augments graph node features to improve GNN performance.
problem Improving graph neural networks' performance on large-scale datasets.
method Iteratively augments node features with gradient-based adversarial perturbations.
result Boosts model performance in node classification, link prediction, and graph classification tasks.
Method verifies robustness of graph neural networks to graph perturbations.
problem Vulnerability of graph neural networks to adversarial attacks.
method Proposes efficient method for certifying robustness to graph perturbations.
result Efficient computation of robustness certificates for graph neural networks.
A new unpooling layer enhances graph generation in molecular models.
problem Efficient graph generation for complex models like molecules.
method Trainable unpooling layer that enlarges and restructures graphs.
result The unpooling layer improves graph generation in molecular models.
Graphs can be fooled by small edge changes, but this work protects them.
problem Adversaries can manipulate graph data to mislead graph classification models.
method We introduce a smoothed graph classification model with a robustness guarantee.
result The smoothed model maintains consistent predictions under small adversarial perturbations.
Rewiring edges subtly improves graph neural networks' robustness.
problem Vulnerability of graph neural networks to subtle structural perturbations.
method Proposes a graph rewiring operation and uses reinforcement learning to learn attack strategies.
result Demonstrates the effectiveness of the proposed framework on real-world graphs.
Survey on adversarial attacks and defenses for images, graphs, and text.
problem Adversarial examples threaten the safety of deep learning applications.
method Review of adversarial attack and defense mechanisms for images, graphs, and text.
result Systematic overview of adversarial attacks and countermeasures.
SEAL improves AL on attributed graphs by combining deep learning and adversarial strategies.
problem Efficient AL on attributed graphs with label sparsity issues.
method SEAL framework using adversarial components for graph embedding and semi-supervised discriminator.
result Superior performance improvements over state-of-the-art baselines.
Graph neural network (GNN), as a powerful representation learning model on graph data, attracts much attention across various disciplines. However, recent studies show that GNN is vulnerable to adversarial attacks. How to make GNN more robust? What are the key vulnerabilities in GNN? How to address the vulnerabilities …
DBGAN learns graph node representations by balancing distribution consistency.
problem Graph representation learning overfits due to ignoring data distribution.
method DBGAN uses a structure-aware prior distribution and bidirectional adversarial learning.
result DBGAN achieves better trade-off between robustness and dimensionality.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.
New method improves graph representations against adversarial attacks.
problem Adversarial robustness in graph contrastive learning.
method Adversarial and edge insertion transformations.
result Promising results in preliminary experiments.
New principle controls graph-informed adversarial discrepancies.
problem Graph-informed adversarial learning for interpolative divergences.
method Proves infimal subadditivity for interpolative divergences.
result Graph-informed adversarial learning is justified for interpolative divergences.
Enhances GCNs' robustness to graph perturbations.
problem Vulnerability of GCNs to graph structure perturbations.
method Generates edge-dithered auxiliary graphs and uses them in an adaptive GCN.
result Significantly improved performance in SSL tasks with noisy inputs and adversarial attacks.
AdaGCN transfers labels across networks via adversarial domain adaptation and graph convolution.
problem Cross-network node classification with limited labeled data.
method Adversarial domain adaptation and graph convolution.
result AdaGCN successfully transfers labels with low labeled data on source networks and significant domain divergence.
Graph attacks can be successful with just a few bad nodes.
problem Adversarial attacks on graph neural networks.
method Identifying and exploiting anchor nodes to compromise graph models.
result A few bad nodes can significantly degrade graph model performance.
PA-GNN enhances GNN robustness against poisoning attacks using clean graph knowledge.
problem Improving robustness of GNNs against poisoning attacks.
method PA-GNN uses a penalized aggregation mechanism and meta-optimization to transfer robustness from clean graphs.
result PA-GNN significantly improves GNN robustness against poisoning attacks on real-world graphs.
SAG is a scalable method for adversarial attacks on GNNs.
problem Scalability and robustness of GNNs to adversarial attacks.
method Decomposing large graphs into smaller partitions, using ADMM for optimization.
result SAG reduces computation and memory overhead for large graphs.
RoGAT enhances GAT robustness against adversarial attacks.
problem Vulnerability of GAT to adversarial attacks.
method Dynamic adjustment of edge weights and features, with an extra attention score.
result RoGAT outperforms other defensive methods in robustness tests.
Adversarial training improves graph autoencoder generalization.
problem Improving graph autoencoder generalization.
method Formulated L2 and L1 adversarial training for graph autoencoders and variational graph autoencoders.
result Adversarial training boosts graph autoencoder and variational graph autoencoder generalization.
New method trims network data to resist adversarial contamination.
problem Adversarial contamination in network data affects statistical and algorithmic performance.
method Proposes a new trimming method operating in model space to address both block and white noise contamination.
result Demonstrates superior performance in simulations compared to direct trimming.
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversari…
The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks. However, the update of parameters in GCNs is only from labeled nodes, lacking the utilization of unlabeled data. In this paper, we apply Virtual Adversarial Training (VAT), an adversaria…
Paper defends sensitive attributes in GNNs from inference attacks.
problem Protecting sensitive attributes in GNNs from inference attacks.
method Proposes adversarial training with TV and Wasserstein distance to locally filter sensitive attributes.
result Framework creates strong defense against inference attacks with minimal performance loss.
GNNGuard defends Graph Neural Networks against structural perturbations.
problem Adversarial attacks on graph neural networks can degrade performance catastrophically.
method Detects and quantifies the relationship between graph structure and node features, then uses this to mitigate attacks.
result GNNGuard outperforms existing defenses by 15.3% on average across various attacks and datasets.
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding dis…