Enhanced Signed Graph Neural Network with Node Polarity

Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit...
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Autor*in:	Jiawang Chen [verfasserIn] Zhi Qiao [verfasserIn] Jun Yan [verfasserIn] Zhenqiang Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	signed network network embedding graph neural network node polarity

Übergeordnetes Werk:	In: Entropy - MDPI AG, 2003, 25(2022), 1, p 38
Übergeordnetes Werk:	volume:25 ; year:2022 ; number:1, p 38

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/e25010038

Katalog-ID:	DOAJ081808739

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allfields	10.3390/e25010038 doi (DE-627)DOAJ081808739 (DE-599)DOAJc98c9dc160c1431c93d36794280d9fed DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jiawang Chen verfasserin aut Enhanced Signed Graph Neural Network with Node Polarity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art. signed network network embedding graph neural network node polarity Science Q Astrophysics Physics Zhi Qiao verfasserin aut Jun Yan verfasserin aut Zhenqiang Wu verfasserin aut In Entropy MDPI AG, 2003 25(2022), 1, p 38 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:25 year:2022 number:1, p 38 https://doi.org/10.3390/e25010038 kostenfrei https://doaj.org/article/c98c9dc160c1431c93d36794280d9fed kostenfrei https://www.mdpi.com/1099-4300/25/1/38 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2022 1, p 38
spelling	10.3390/e25010038 doi (DE-627)DOAJ081808739 (DE-599)DOAJc98c9dc160c1431c93d36794280d9fed DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jiawang Chen verfasserin aut Enhanced Signed Graph Neural Network with Node Polarity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art. signed network network embedding graph neural network node polarity Science Q Astrophysics Physics Zhi Qiao verfasserin aut Jun Yan verfasserin aut Zhenqiang Wu verfasserin aut In Entropy MDPI AG, 2003 25(2022), 1, p 38 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:25 year:2022 number:1, p 38 https://doi.org/10.3390/e25010038 kostenfrei https://doaj.org/article/c98c9dc160c1431c93d36794280d9fed kostenfrei https://www.mdpi.com/1099-4300/25/1/38 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2022 1, p 38
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allfieldsSound	10.3390/e25010038 doi (DE-627)DOAJ081808739 (DE-599)DOAJc98c9dc160c1431c93d36794280d9fed DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jiawang Chen verfasserin aut Enhanced Signed Graph Neural Network with Node Polarity 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art. signed network network embedding graph neural network node polarity Science Q Astrophysics Physics Zhi Qiao verfasserin aut Jun Yan verfasserin aut Zhenqiang Wu verfasserin aut In Entropy MDPI AG, 2003 25(2022), 1, p 38 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:25 year:2022 number:1, p 38 https://doi.org/10.3390/e25010038 kostenfrei https://doaj.org/article/c98c9dc160c1431c93d36794280d9fed kostenfrei https://www.mdpi.com/1099-4300/25/1/38 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2022 1, p 38
language	English
source	In Entropy 25(2022), 1, p 38 volume:25 year:2022 number:1, p 38
sourceStr	In Entropy 25(2022), 1, p 38 volume:25 year:2022 number:1, p 38
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	signed network network embedding graph neural network node polarity Science Q Astrophysics Physics
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container_title	Entropy
authorswithroles_txt_mv	Jiawang Chen @@aut@@ Zhi Qiao @@aut@@ Jun Yan @@aut@@ Zhenqiang Wu @@aut@@
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callnumber-first	Q - Science
author	Jiawang Chen
spellingShingle	Jiawang Chen misc QB460-466 misc QC1-999 misc signed network misc network embedding misc graph neural network misc node polarity misc Science misc Q misc Astrophysics misc Physics Enhanced Signed Graph Neural Network with Node Polarity
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topic_title	QB460-466 QC1-999 Enhanced Signed Graph Neural Network with Node Polarity signed network network embedding graph neural network node polarity
topic	misc QB460-466 misc QC1-999 misc signed network misc network embedding misc graph neural network misc node polarity misc Science misc Q misc Astrophysics misc Physics
topic_unstemmed	misc QB460-466 misc QC1-999 misc signed network misc network embedding misc graph neural network misc node polarity misc Science misc Q misc Astrophysics misc Physics
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title	Enhanced Signed Graph Neural Network with Node Polarity
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title_auth	Enhanced Signed Graph Neural Network with Node Polarity
abstract	Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.
abstractGer	Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.
abstract_unstemmed	Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.
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title_short	Enhanced Signed Graph Neural Network with Node Polarity
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