Survey of Graph Neural Network for Internet of Things and NextG Networks

• URL: http://arxiv.org/abs/2405.17309v1
• Date: Mon, 27 May 2024 16:10:49 GMT
• Title: Survey of Graph Neural Network for Internet of Things and NextG Networks
• Authors: Sabarish Krishna Moorthy, Jithin Jagannath,
• Abstract summary: Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights. This survey provides a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems.
• Score: 3.591122855617648
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficiently use the resources available and meet the users' requirements. Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights which inherently exhibit complex network structures due to its high performance and accuracy, scalability, adaptability, and resource efficiency. There is a lack of a comprehensive survey that focuses on the applications and advances GNN has made in the context of IoT and Next Generation (NextG) networks. To bridge that gap, this survey starts by providing a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Then we provide a comprehensive survey of the advancements in applying GNNs for IoT from the perspective of data fusion and intrusion detection. Thereafter, we survey the impact GNN has made in improving spectrum awareness. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems. Through this survey, we aim to provide a comprehensive resource for researchers to learn more about GNN in the context of wireless networks, and understand its state-of-the-art use cases while contrasting to other machine learning approaches. Finally, we also discussed the challenges and wide range of future research directions to further motivate the use of GNN for IoT and NextG Networks.

Related papers

• Information Flow in Graph Neural Networks: A Clinical Triage Use Case [49.86931948849343]
  Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. We investigate how the flow of embedding information within GNNs affects the prediction of links in Knowledge Graphs (KGs) Our results demonstrate that incorporating domain knowledge into the GNN connectivity leads to better performance than using the same connectivity as the KG or allowing unconstrained embedding propagation.
  arXiv  Detail & Related papers  (2023-09-12T09:18:12Z)
• A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware [30.525912505620685]
  Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability challenges when it comes to real-world applications. We provide a taxonomy of GNN acceleration, review the existing approaches, and suggest future research directions.
  arXiv  Detail & Related papers  (2023-06-24T20:20:45Z)
• 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)
• Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions [62.48370728401775]
  We provide an overview of the interplay between graph neural networks (GNNs) and wireless communications. GNNs for wireless communications (GNN4Com) and wireless communications for GNNs (Com4GNN) We highlight potential research directions to promote future research endeavors for GNNs in wireless communications.
  arXiv  Detail & Related papers  (2022-12-08T02:57:55Z)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends [115.84291569988748]
  Graph neural networks (GNNs) have emerged as competent graph learning methods for diverse real-world scenarios. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness.
  arXiv  Detail & Related papers  (2022-05-16T02:21:09Z)
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability [59.80140875337769]
  Graph Neural Networks (GNNs) have made rapid developments in the recent years. GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data. This paper gives a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability.
  arXiv  Detail & Related papers  (2022-04-18T21:41:07Z)
• Graph Neural Networks in IoT: A Survey [9.257834364029547]
  The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives. Deep learning models have been extensively employed in solving IoT tasks. Graph Neural Networks (GNNs) have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks.
  arXiv  Detail & Related papers  (2022-03-29T22:27:59Z)
• Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities [4.4568884144849985]
  Graph neural networks (GNNs) have shown outstanding applications in many fields where data is fundamentally represented as graphs. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real networks. This article comprises a brief tutorial on GNNs and their possible applications to communication networks.
  arXiv  Detail & Related papers  (2021-12-29T19:09:42Z)
• Scaling Graph-based Deep Learning models to larger networks [2.946140899052065]
  Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. This paper presents a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.
  arXiv  Detail & Related papers  (2021-10-04T09:04:19Z)
• Computing Graph Neural Networks: A Survey from Algorithms to Accelerators [2.491032752533246]
  Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. This paper aims to make two main contributions: a review of the field of GNNs is presented from the perspective of computing. An in-depth analysis of current software and hardware acceleration schemes is provided.
  arXiv  Detail & Related papers  (2020-09-30T22:29:27Z)
• Attentive Graph Neural Networks for Few-Shot Learning [74.01069516079379]
  Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few-shot learning tasks. Despite its powerful capacity to learn and generalize the model from few samples, GNN usually suffers from severe over-fitting and over-smoothing as the model becomes deep. We propose a novel Attentive GNN to tackle these challenges, by incorporating a triple-attention mechanism.
  arXiv  Detail & Related papers  (2020-07-14T07:43:09Z)

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.