The Eisenberg and Noe (EN) model has been widely adopted in the systemic risk management for financial networks. In this paper, we propose a unified EN (U‐EN) model, which incorporates both liquidation and bankruptcy costs. We show that the U‐EN model is polynomial‐time solvable and develop an efficient greedy algorithm to solve it. Then we consider identifying the optimal bailout strategy based on stress testing background and propose a binary EN model with bailout budget constraint (B‐EN‐B). The B‐EN‐B model is shown to be NP‐hard. We present analysis on the parameter selection and design some preprocessing procedures correspondingly. A sequential coefficient strengthening algorithm is designed to solve the B‐EN‐B model. Global convergence of the algorithm is established. Moreover, we show that the systemic risk level obtained from the B‐EN‐B model can be used as a precaution for the social planner. Experiments based on both simulated data and data from the Chinese listed banks' network are reported to demonstrate the efficiency of the proposed algorithms.
Efficient Algorithms towards Network Intervention
Research suggests that social relationships have substantial impacts on individuals’ health outcomes. Network intervention, through careful planning, can assist a network of users to build healthy relationships. However, most previous work is not designed to assist such planning by carefully examining and improving multiple network characteristics. In this paper, we propose and evaluate algorithms that facilitate network intervention planning through simultaneous optimization of network degree, closeness, betweenness, and local clustering coefficient, under scenarios involving Network Intervention with Limited Degradation - for Single target (NILD-S) and Network Intervention with Limited Degradation - for Multiple targets (NILD-M). We prove that NILD-S and NILD-M are NP-hard and cannot be approximated within any ratio in polynomial time unless P=NP. We propose the Candidate Re-selection with Preserved Dependency (CRPD) algorithm for NILD-S, and the Objective-aware Intervention edge Selection and Adjustment (OISA) algorithm for NILD-M. Various pruning strategies are designed to boost the efficiency of the proposed algorithms. Extensive experiments on various real social networks collected from public schools and Web and an empirical study are conducted to show that CRPD and OISA outperform the baselines in both efficiency and effectiveness.
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- NSF-PAR ID:
- 10170813
- Date Published:
- Journal Name:
- Proceedings of The Web Conference 2020
- Page Range / eLocation ID:
- 2021 to 2031
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3366423.3380269
