Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs

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-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="chen-ieq1-3094549.gif"/></alternatives></inline-formula> 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-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="chen-ieq2-3094549.gif"/></alternatives></inline-formula> 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.

[1]  Yanping Wu,et al.  Edge Manipulation Approaches for K-Core Minimization: Metrics and Analytics , 2023, IEEE Transactions on Knowledge and Data Engineering.

[2]  Zhibin Niu,et al.  Delinquent Events Prediction in Temporal Networked-Guarantee Loans , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Dawei Cheng,et al.  Contagious Chain Risk Rating for Networked-guarantee Loans , 2020, KDD.

[4]  Ying Zhang,et al.  Risk Guarantee Prediction in Networked-Loans , 2020, IJCAI.

[5]  Zhibin Niu,et al.  iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans , 2020, 2020 IEEE Conference on Visual Analytics Science and Technology (VAST).

[6]  Dawei Cheng,et al.  Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation , 2019, IJCAI.

[7]  Fei Tan,et al.  A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Arijit Khan,et al.  An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs , 2019, Proc. VLDB Endow..

[9]  Jeffrey Xu Yu,et al.  Improved Algorithms for Maximal Clique Search in Uncertain Networks , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[10]  Yuchen Li,et al.  Influence Maximization on Social Graphs: A Survey , 2018, IEEE Transactions on Knowledge and Data Engineering.

[11]  Piet Van Mieghem,et al.  Nodal vulnerability to targeted attacks in power grids , 2018, Applied Network Science.

[12]  Dawei Cheng,et al.  Prediction Defaults for Networked-guarantee Loans , 2017, 2018 24th International Conference on Pattern Recognition (ICPR).

[13]  Francesco Bonchi,et al.  Conditional Reliability in Uncertain Graphs , 2016, IEEE Transactions on Knowledge and Data Engineering.

[14]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[15]  Xiaoyang Wang,et al.  Identifying Top-K Influential Nodes in Networks , 2017, CIKM.

[16]  Xuemin Lin,et al.  Bring Order into the Samples: A Novel Scalable Method for Influence Maximization , 2017, IEEE Transactions on Knowledge and Data Engineering.

[17]  Ei Ei Thu,et al.  Transforming Model Oriented Program into Android Source Code Based on Drools Rule Engine , 2017 .

[18]  Xuemin Lin,et al.  Efficient Distance-Aware Influence Maximization in Geo-Social Networks , 2017, IEEE Transactions on Knowledge and Data Engineering.

[19]  Lei Zheng,et al.  Joint Deep Modeling of Users and Items Using Reviews for Recommendation , 2017, WSDM.

[20]  Liqing Zhang,et al.  Credit Card Fraud Detection Using Convolutional Neural Networks , 2016, ICONIP.

[21]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[22]  Xuemin Lin,et al.  Efficient Probabilistic Supergraph Search , 2016, IEEE Transactions on Knowledge and Data Engineering.

[23]  Qiang Yang,et al.  The Lifecycle and Cascade of WeChat Social Messaging Groups , 2015, WWW.

[24]  Xiaokui Xiao,et al.  Influence Maximization in Near-Linear Time: A Martingale Approach , 2015, SIGMOD Conference.

[25]  Kartik Anand,et al.  Filling in the blanks: network structure and interbank contagion , 2014, SSRN Electronic Journal.

[26]  Spiros Bougheas,et al.  Complex Financial Networks and Systemic Risk: A Review , 2014, SSRN Electronic Journal.

[27]  Jian Xu,et al.  A Forensic Analysis Method for Redis Database based on RDB and AOF File , 2014, J. Comput..

[28]  Arunabha Sen,et al.  Identification of K most vulnerable nodes in multi-layered network using a new model of interdependency , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[29]  Christian Borgs,et al.  Maximizing Social Influence in Nearly Optimal Time , 2012, SODA.

[30]  Aristides Gionis,et al.  Fast Reliability Search in Uncertain Graphs , 2014, EDBT.

[31]  Yan Jia,et al.  Identifying Vulnerable Nodes of Complex Networks in Cascading Failures Induced by Node-Based Attacks , 2013 .

[32]  Véronique Van Vlasselaer,et al.  Using social network knowledge for detecting spider constructions in social security fraud , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[33]  Tamir Tassa,et al.  Injecting Uncertainty in Graphs for Identity Obfuscation , 2012, Proc. VLDB Endow..

[34]  Ming Xu,et al.  Determinants of the guarantee circles: The case of Chinese listed firms , 2012 .

[35]  H. Brendan McMahan,et al.  Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization , 2011, AISTATS.

[36]  Haixun Wang,et al.  Distance-Constraint Reachability Computation in Uncertain Graphs , 2011, Proc. VLDB Endow..

[37]  B. Engelmann,et al.  The Basel II risk parameters : estimation, validation, stress testing--with applications to loan risk management , 2011 .

[38]  George Kollios,et al.  k-nearest neighbors in uncertain graphs , 2010, Proc. VLDB Endow..

[39]  Franklin Allen,et al.  Financial Connections and Systemic Risk , 2010 .

[40]  A. Lo,et al.  Consumer Credit Risk Models Via Machine-Learning Algorithms , 2010 .

[41]  W. S. Chow,et al.  Social network, social trust and shared goals in organizational knowledge sharing , 2008, Inf. Manag..

[42]  Edith Cohen,et al.  Summarizing data using bottom-k sketches , 2007, PODC '07.

[43]  Jukka-Pekka Onnela,et al.  Complex networks in the study of financial and social systems , 2006 .

[44]  Carl D. Meyer,et al.  Deeper Inside PageRank , 2004, Internet Math..

[45]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[46]  Bart Baesens,et al.  Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..

[47]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[48]  Serge Abiteboul,et al.  On the representation and querying of sets of possible worlds , 1987, SIGMOD '87.

[49]  G. Masters.  A rasch model for partial credit scoring , 1982 .