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GNR: A universal and efficient node ranking model for various tasks based on graph neural networks.

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Title: GNR: A universal and efficient node ranking model for various tasks based on graph neural networks.
Authors: Qu, Hongbo1 (AUTHOR), Song, Yu-Rong1,2 (AUTHOR) songyr@njupt.edu.cn, Li, Ruqi1 (AUTHOR), Li, Min2 (AUTHOR)
Source: Physica A. Dec2023:Part 2, Vol. 632, pN.PAG-N.PAG. 1p.
Subjects: Spreading cortical depression, Infection
Abstract: Node ranking is an essential problem in complex networks, which aims to identify influential or central nodes in a graph. Existing methods for node ranking are either computationally expensive or suboptimal for different networks and multiple tasks. In this paper, we propose GNR, a graph neural network-based node ranking model that can sort nodes quickly and efficiently for any type of networks. To achieve this, GNR takes into account three factors to calculate the importance of nodes and train the model: degree, infection score, and dismantling score, which capture the local and global structure of the network and the effect of nodes on network propagation and dismantling. We conduct extensive experiments on 16 real networks, and compare GNR with other centrality-based node ranking models on three tasks: network dismantling, virus propagation, and information spreading. The results show that GNR performs better in most cases and validates the effectiveness and superiority of our model. • Proposing a graph neural network-based node ranking model. • GNR fuses different node importance metrics for generality. • GNR is efficient and scalable for any type of networks. • GNR outperforms other centrality-based methods in various tasks. [ABSTRACT FROM AUTHOR]
Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: GNR: A universal and efficient node ranking model for various tasks based on graph neural networks.
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  Data: <searchLink fieldCode="AR" term="%22Qu%2C+Hongbo%22">Qu, Hongbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Yu-Rong%22">Song, Yu-Rong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> songyr@njupt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ruqi%22">Li, Ruqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Min%22">Li, Min</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Dec2023:Part 2, Vol. 632, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Spreading+cortical+depression%22">Spreading cortical depression</searchLink><br /><searchLink fieldCode="DE" term="%22Infection%22">Infection</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Node ranking is an essential problem in complex networks, which aims to identify influential or central nodes in a graph. Existing methods for node ranking are either computationally expensive or suboptimal for different networks and multiple tasks. In this paper, we propose GNR, a graph neural network-based node ranking model that can sort nodes quickly and efficiently for any type of networks. To achieve this, GNR takes into account three factors to calculate the importance of nodes and train the model: degree, infection score, and dismantling score, which capture the local and global structure of the network and the effect of nodes on network propagation and dismantling. We conduct extensive experiments on 16 real networks, and compare GNR with other centrality-based node ranking models on three tasks: network dismantling, virus propagation, and information spreading. The results show that GNR performs better in most cases and validates the effectiveness and superiority of our model. • Proposing a graph neural network-based node ranking model. • GNR fuses different node importance metrics for generality. • GNR is efficient and scalable for any type of networks. • GNR outperforms other centrality-based methods in various tasks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Physica A is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.physa.2023.129339
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – SubjectFull: Spreading cortical depression
        Type: general
      – SubjectFull: Infection
        Type: general
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      – TitleFull: GNR: A universal and efficient node ranking model for various tasks based on graph neural networks.
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            NameFull: Qu, Hongbo
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            NameFull: Song, Yu-Rong
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              M: 12
              Text: Dec2023:Part 2
              Type: published
              Y: 2023
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              Value: 632
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