DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise

• URL: http://arxiv.org/abs/2404.09207v1
• Date: Sun, 14 Apr 2024 10:04:44 GMT
• Title: DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
• Authors: Tai Hasegawa, Sukwon Yun, Xin Liu, Yin Jun Phua, Tsuyoshi Murata,
• Abstract summary: Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. Recent research has revealed that real-world graphs often contain noise, and GNNs are susceptible to noise in the graph. We present DEGNN, a novel GNN model designed to adeptly mitigate noise in both edges and node features.
• Score: 5.048629544493508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. However, recent research has revealed that real-world graphs often contain noise, and GNNs are susceptible to noise in the graph. To address this issue, several Graph Structure Learning (GSL) models have been introduced. While GSL models are tailored to enhance robustness against edge noise through edge reconstruction, a significant limitation surfaces: their high reliance on node features. This inherent dependence amplifies their susceptibility to noise within node features. Recognizing this vulnerability, we present DEGNN, a novel GNN model designed to adeptly mitigate noise in both edges and node features. The core idea of DEGNN is to design two separate experts: an edge expert and a node feature expert. These experts utilize self-supervised learning techniques to produce modified edges and node features. Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs. Notably, the modification process can be trained end-to-end, empowering DEGNN to adjust dynamically and achieves optimal edge and node representations for specific tasks. Comprehensive experiments demonstrate DEGNN's efficacy in managing noise, both in original real-world graphs and in graphs with synthetic noise.

Related papers

• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN [36.045702771828736]
  Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. Recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously modifying the graph structure. We propose an unsupervised pipeline, named STABLE, to optimize the graph structure.
  arXiv  Detail & Related papers  (2022-06-30T10:02:32Z)
• 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)
• Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels [24.25945793671978]
  We study a novel problem of developing robust GNNs on noisy graphs with limited labeled nodes. Our analysis shows that both the noisy edges and limited labeled nodes could harm the message-passing mechanism of GNNs. We propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph.
  arXiv  Detail & Related papers  (2022-01-01T19:00:26Z)
• Dual GNNs: Graph Neural Network Learning with Limited Supervision [33.770877823910176]
  We propose a novel Dual GNN learning framework to address this challenge task. By integrating the two modules in a dual GNN learning framework, we perform joint learning in an end-to-end fashion.
  arXiv  Detail & Related papers  (2021-06-29T23:52:25Z)
• Edgeless-GNN: Unsupervised Inductive Edgeless Network Embedding [7.391641422048645]
  We study the problem of embedding edgeless nodes such as users who newly enter the underlying network. We propose Edgeless-GNN, a new framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised inductive learning.
  arXiv  Detail & Related papers  (2021-04-12T06:37:31Z)
• Learning to Drop: Robust Graph Neural Network via Topological Denoising [50.81722989898142]
  We propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of Graph Neural Networks (GNNs) PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. We show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
  arXiv  Detail & Related papers  (2020-11-13T18:53:21Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)
• EdgeNets:Edge Varying Graph Neural Networks [179.99395949679547]
  This paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs)
  arXiv  Detail & Related papers  (2020-01-21T15:51:17Z)

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.