Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs

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

Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs

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

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (2), pp. 1460-1472
Issue Date:: 2023-02-01

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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 https://orcid.org/0000-0002-1454-8240
dc.date.accessioned	2024-03-21T02:36:09Z
dc.date.available	2024-03-21T02:36:09Z
dc.date.issued	2023-02-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2023, 35, (2), pp. 1460-1472
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/176964
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 identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the top-kk vulnerable nodes detection problem in uncertain graphs. We formally define the model and prove it hardness. A sampling-based approach is first proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results. Novel optimization techniques and a bottom-kk sketch based approach are further developed to scale for large networks. We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. Moreover, to further verify the advantages of our model, we integrate the proposed techniques with our loan risk control system, which is deployed in the collaborated bank. Particularly, we show that our proposed model can better estimate the default risks of enterprises compared to the state-of-the-art techniques.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2021.3094549
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	in_progress	*
dc.date.updated	2024-03-21T02:36:08Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	2

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 identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the top-kk vulnerable nodes detection problem in uncertain graphs. We formally define the model and prove it hardness. A sampling-based approach is first proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results. Novel optimization techniques and a bottom-kk sketch based approach are further developed to scale for large networks. We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. Moreover, to further verify the advantages of our model, we integrate the proposed techniques with our loan risk control system, which is deployed in the collaborated bank. Particularly, we show that our proposed model can better estimate the default risks of enterprises compared to the state-of-the-art techniques.

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http://hdl.handle.net/10453/176964