Improving Graph Neural Networks via Adversarial Robustness Evaluation

• URL: http://arxiv.org/abs/2412.10850v1
• Date: Sat, 14 Dec 2024 14:47:20 GMT
• Title: Improving Graph Neural Networks via Adversarial Robustness Evaluation
• Authors: Yongyu Wang,
• Abstract summary: Graph Neural Networks (GNNs) are one of the most powerful types of neural network architectures. However, GNNs are vulnerable to noise in the graph structure. In this paper, we propose using adversarial robustness evaluation to select a small subset of robust nodes that are less affected by noise.
• Score: 2.1937382384136637
• License:
• Abstract: Graph Neural Networks (GNNs) are currently one of the most powerful types of neural network architectures. Their advantage lies in the ability to leverage both the graph topology, which represents the relationships between samples, and the features of the samples themselves. However, the given graph topology often contains noisy edges, and GNNs are vulnerable to noise in the graph structure. This issue remains unresolved. In this paper, we propose using adversarial robustness evaluation to select a small subset of robust nodes that are less affected by noise. We then only feed the features of these robust nodes, along with the KNN graph constructed from these nodes, into the GNN for classification. Additionally, we compute the centroids for each class. For the remaining non-robust nodes, we assign them to the class whose centroid is closest to them. Experimental results show that this method significantly improves the accuracy of GNNs.

Related papers

• GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification [1.857645719601748]
  Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data. We show that even the most expressive GNN may fail to learn in absence of node attributes and without using explicit label information as input. We propose a straightforward approach, referred to as GNN-MultiFix, that integrates the feature, label, and positional information of a node.
  arXiv  Detail & Related papers  (2024-11-21T12:59:39Z)
• Classifying Nodes in Graphs without GNNs [50.311528896010785]
  We propose a fully GNN-free approach for node classification, not requiring them at train or test time. Our method consists of three key components: smoothness constraints, pseudo-labeling iterations and neighborhood-label histograms.
  arXiv  Detail & Related papers  (2024-02-08T18:59:30Z)
• Degree-based stratification of nodes in Graph Neural Networks [66.17149106033126]
  We modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods.
  arXiv  Detail & Related papers  (2023-12-16T14:09:23Z)
• Self-attention Dual Embedding for Graphs with Heterophily [6.803108335002346]
  A number of real-world graphs are heterophilic, and this leads to much lower classification accuracy using standard GNNs. We design a novel GNN which is effective for both heterophilic and homophilic graphs. We evaluate our algorithm on real-world graphs containing thousands to millions of nodes and show that we achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-05-28T09:38:28Z)
• Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily [58.76759997223951]
  We propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs. We also propose a Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets.
  arXiv  Detail & Related papers  (2022-03-19T14:26:43Z)
• Graph Neural Networks with Feature and Structure Aware Random Walk [7.143879014059894]
  We show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models. We develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes.
  arXiv  Detail & Related papers  (2021-11-19T08:54:21Z)
• GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks [28.92347073786722]
  Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. We propose a novel framework, GraphSMOTE, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesize in this space to assure genuineness.
  arXiv  Detail & Related papers  (2021-03-16T03:23:55Z)
• Scalable Graph Neural Networks for Heterogeneous Graphs [12.44278942365518]
  Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities.
  arXiv  Detail & Related papers  (2020-11-19T06:03:35Z)
• Graph Neural Networks: Architectures, Stability and Transferability [176.3960927323358]
  Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They are generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters.
  arXiv  Detail & Related papers  (2020-08-04T18:57:36Z)
• Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks [183.97265247061847]
  We leverage graph signal processing to characterize the representation space of graph neural networks (GNNs) We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. We also study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
  arXiv  Detail & Related papers  (2020-03-08T13:02:15Z)
• Bilinear Graph Neural Network with Neighbor Interactions [106.80781016591577]
  Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
  arXiv  Detail & Related papers  (2020-02-10T06:43:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.