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Found 23 papers, 12 papers with code
Hierarchical Randomized Smoothing
Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise before classification.
Probing Graph Representations
Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models.
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.
Are Defenses for Graph Neural Networks Robust?
A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs).
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes.
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
In this paper, we extensively study this phenomenon for graph data.
Localized Randomized Smoothing for Collective Robustness Certification
We further show that this approach is beneficial for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e. g. based on their proximity in the image).
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius.
Robustness of Graph Neural Networks at Scale
Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications.
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features.
Provably Robust Transfer
Our experiments show how adversarial training on the source domain affects robustness on source and target domain, and we propose the first provably robust transfer learning models.
Collective Robustness Certificates
In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.
Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness.
Adversarial Attacks on Node Embeddings
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.
Pitfalls of Graph Neural Network Evaluation
We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.
Ranked #1 on
Node Classification
on MS ACADEMIC
General Classification
Node Classification on Non-Homophilic (Heterophilic) Graphs
Adversarial Attacks on Node Embeddings via Graph Poisoning
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.
Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.
NetGAN: Generating Graphs via Random Walks
NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.
GraphGAN: Generating Graphs via Random Walks
Moreover, GraphGAN learns a semantic mapping from the latent input space to the generated graph's properties.
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.
