Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs (Extended abstract)

Cheng, D; Chen, C; Wang, X; Xiang, S

Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs (Extended abstract)

Cheng, D Chen, C Wang, X Xiang, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022, 00, pp. 1547-1548
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Cheng, D
dc.contributor.author	Chen, C
dc.contributor.author	Wang, X
dc.contributor.author	Xiang, S
dc.date	2022-05-09
dc.date.accessioned	2023-06-28T03:59:48Z
dc.date.available	2023-06-28T03:59:48Z
dc.date.issued	2022
dc.identifier.citation	2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022, 00, pp. 1547-1548
dc.identifier.isbn	978-1-6654-0883-7
dc.identifier.issn	1063-6382
dc.identifier.issn	2375-026X
dc.identifier.uri	http://hdl.handle.net/10453/170939
dc.description.abstract	Uncertain graphs have been widely used to model complex linked data in many applications such as guaranteed loan networks and power grids In these networks a node usually has a certain chance of default due to self factors or the influence from upstream nodes For regulatory authorities it is critical to efficiently and accurately identify the vulnerable nodes i e nodes with high default risk such that people could pay more attention to these nodes for the purpose of risk management In this paper we propose and investigate the top k vulnerable nodes detection problem in uncertain graphs Due to the hardness of the problem sampling based methods are proposed with tight theoretical guarantee We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2022 IEEE 38th International Conference on Data Engineering (ICDE)
dc.relation.ispartof	IEEE International Conference on Data Engineering
dc.relation.isbasedon	10.1109/icde53745.2022.00150
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs (Extended abstract)
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Kuala Lumpur, Malaysia
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-06-28T03:59:47Z
pubs.finish-date	2022-05-12
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2022-05-09
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Uncertain graphs have been widely used to model complex linked data in many applications such as guaranteed loan networks and power grids In these networks a node usually has a certain chance of default due to self factors or the influence from upstream nodes For regulatory authorities it is critical to efficiently and accurately identify the vulnerable nodes i e nodes with high default risk such that people could pay more attention to these nodes for the purpose of risk management In this paper we propose and investigate the top k vulnerable nodes detection problem in uncertain graphs Due to the hardness of the problem sampling based methods are proposed with tight theoretical guarantee We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks

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http://hdl.handle.net/10453/170939