Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks

Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type...
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Autor*in:	Jianjun Wu [verfasserIn] Yuxue Hu [verfasserIn] Zhongqiang Huang [verfasserIn] Junsong Li [verfasserIn] Xiang Li [verfasserIn] Ying Sha [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	heterogeneous social network link prediction enhancing predictive expert

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 22, p 12437
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:22, p 12437

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app132212437

Katalog-ID:	DOAJ101278152

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spelling	10.3390/app132212437 doi (DE-627)DOAJ101278152 (DE-599)DOAJ632bdc7784bd436ea1c090b1fe8780df DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Jianjun Wu verfasserin aut Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods. heterogeneous social network link prediction enhancing predictive expert Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Yuxue Hu verfasserin aut Zhongqiang Huang verfasserin aut Junsong Li verfasserin aut Xiang Li verfasserin aut Ying Sha verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 22, p 12437 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:22, p 12437 https://doi.org/10.3390/app132212437 kostenfrei https://doaj.org/article/632bdc7784bd436ea1c090b1fe8780df kostenfrei https://www.mdpi.com/2076-3417/13/22/12437 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 13 2023 22, p 12437
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allfieldsGer	10.3390/app132212437 doi (DE-627)DOAJ101278152 (DE-599)DOAJ632bdc7784bd436ea1c090b1fe8780df DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Jianjun Wu verfasserin aut Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods. heterogeneous social network link prediction enhancing predictive expert Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Yuxue Hu verfasserin aut Zhongqiang Huang verfasserin aut Junsong Li verfasserin aut Xiang Li verfasserin aut Ying Sha verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 22, p 12437 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:22, p 12437 https://doi.org/10.3390/app132212437 kostenfrei https://doaj.org/article/632bdc7784bd436ea1c090b1fe8780df kostenfrei https://www.mdpi.com/2076-3417/13/22/12437 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 13 2023 22, p 12437
allfieldsSound	10.3390/app132212437 doi (DE-627)DOAJ101278152 (DE-599)DOAJ632bdc7784bd436ea1c090b1fe8780df DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Jianjun Wu verfasserin aut Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods. heterogeneous social network link prediction enhancing predictive expert Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Yuxue Hu verfasserin aut Zhongqiang Huang verfasserin aut Junsong Li verfasserin aut Xiang Li verfasserin aut Ying Sha verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 22, p 12437 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:22, p 12437 https://doi.org/10.3390/app132212437 kostenfrei https://doaj.org/article/632bdc7784bd436ea1c090b1fe8780df kostenfrei https://www.mdpi.com/2076-3417/13/22/12437 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 13 2023 22, p 12437
language	English
source	In Applied Sciences 13(2023), 22, p 12437 volume:13 year:2023 number:22, p 12437
sourceStr	In Applied Sciences 13(2023), 22, p 12437 volume:13 year:2023 number:22, p 12437
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	heterogeneous social network link prediction enhancing predictive expert Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Jianjun Wu @@aut@@ Yuxue Hu @@aut@@ Zhongqiang Huang @@aut@@ Junsong Li @@aut@@ Xiang Li @@aut@@ Ying Sha @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Jianjun Wu
spellingShingle	Jianjun Wu misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc heterogeneous social network misc link prediction misc enhancing predictive expert misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
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topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc heterogeneous social network misc link prediction misc enhancing predictive expert misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
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title_full	Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
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title_sort	enhancing predictive expert method for link prediction in heterogeneous information social networks
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title_auth	Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
abstract	Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.
abstractGer	Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.
abstract_unstemmed	Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.
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title_short	Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
url	https://doi.org/10.3390/app132212437 https://doaj.org/article/632bdc7784bd436ea1c090b1fe8780df https://www.mdpi.com/2076-3417/13/22/12437 https://doaj.org/toc/2076-3417
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Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks

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