Attentional Graph Neural Networks for Robust Massive Network Localization

• URL: http://arxiv.org/abs/2311.16856v2
• Date: Wed, 14 Feb 2024 11:21:32 GMT
• Title: Attentional Graph Neural Networks for Robust Massive Network Localization
• Authors: Wenzhong Yan, Juntao Wang, Feng Yin, Yang Tian, Abdelhak M. Zoubir
• Abstract summary: Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. This paper integrates GNNs with attention mechanism to tackle a challenging nonlinear regression problem: network localization. We first introduce a novel network localization method based on graph convolutional network (GCN), which exhibits exceptional precision even under severe non-line-of-sight (NLOS) conditions.
• Score: 20.416879207269446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. However, their application in regression tasks remains underexplored. To tap the potential of GNNs in regression, this paper integrates GNNs with attention mechanism, a technique that revolutionized sequential learning tasks with its adaptability and robustness, to tackle a challenging nonlinear regression problem: network localization. We first introduce a novel network localization method based on graph convolutional network (GCN), which exhibits exceptional precision even under severe non-line-of-sight (NLOS) conditions, thereby diminishing the need for laborious offline calibration or NLOS identification. We further propose an attentional graph neural network (AGNN) model, aimed at improving the limited flexibility and mitigating the high sensitivity to the hyperparameter of the GCN-based method. The AGNN comprises two crucial modules, each designed with distinct attention architectures to address specific issues associated with the GCN-based method, rendering it more practical in real-world scenarios. Experimental results substantiate the efficacy of our proposed GCN-based method and AGNN model, as well as the enhancements of AGNN model. Additionally, we delve into the performance improvements of AGNN model by analyzing it from the perspectives of dynamic attention and computational complexity.

Related papers

• A General Recipe for Contractive Graph Neural Networks -- Technical Report [4.14360329494344]
  Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data. GNNs often face challenges related to stability, generalization, and robustness to noise and adversarial attacks. This paper presents a novel method for inducing contractive behavior in any GNN through SVD regularization.
  arXiv  Detail & Related papers  (2024-11-04T00:05:21Z)
• Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies [15.037300421748107]
  spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of neurons. This work examines the unique properties and benefits of spiking dynamics in enhancing graph representation learning. We propose a spike-based graph neural network model that incorporates spiking dynamics, enhanced by a novel spatial-temporal feature normalization (STFN) technique.
  arXiv  Detail & Related papers  (2024-07-30T02:53:26Z)
• SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning [42.716744098170835]
  We propose a novel framework named Spike-induced Graph Neural Network (SiGNN) for learning enhanced spatialtemporal representations on dynamic graphs. Benefiting from the TA mechanism, SiGNN not only effectively exploits the temporal dynamics of SNNs but also adeptly circumvents the representational constraints imposed by the binary nature of spikes. Extensive experiments on various real-world dynamic graph datasets demonstrate the superior performance of SiGNN in the node classification task.
  arXiv  Detail & Related papers  (2024-03-11T05:19:43Z)
• Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias [75.44877675117749]
  We propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph datasets Benchmark.
  arXiv  Detail & Related papers  (2023-09-26T13:09:43Z)
• Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective [22.82625446308785]
  We propose a novel view for understanding graph neural networks (GNNs) In this work, we focus on deep GNNs and propose a novel view for understanding them. We design a more powerful GNN: coupling graph neural network (CoGNet)
  arXiv  Detail & Related papers  (2023-01-25T12:02:12Z)
• PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors [3.2780036095732035]
  Graph neural networks (GNNs) have emerged as a promising learning paradigm in learning graph-structured data. This paper conducts a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy.
  arXiv  Detail & Related papers  (2022-12-07T06:14:14Z)
• CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks [59.692017490560275]
  Adversarial training has been widely demonstrated to improve model's robustness against adversarial attacks. It remains unclear how the adversarial training could improve the generalization abilities of GNNs in the graph analytics problem. We construct the co-adversarial perturbation (CAP) optimization problem in terms of weights and features, and design the alternating adversarial perturbation algorithm to flatten the weight and feature loss landscapes alternately.
  arXiv  Detail & Related papers  (2021-10-28T02:28:13Z)
• 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)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.