research-article

SNARE: a link analytic system for graph labeling and risk detection

Authors:

Markus G. Anderle,

David M. Steier,

Christos FaloutsosAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1265 - 1274

https://doi.org/10.1145/1557019.1557155

Published: 28 June 2009 Publication History

Abstract

Classifying nodes in networks is a task with a wide range of applications. It can be particularly useful in anomaly and fraud detection. Many resources are invested in the task of fraud detection due to the high cost of fraud, and being able to automatically detect potential fraud quickly and precisely allows human investigators to work more efficiently. Many data analytic schemes have been put into use; however, schemes that bolster link analysis prove promising. This work builds upon the belief propagation algorithm for use in detecting collusion and other fraud schemes. We propose an algorithm called SNARE (Social Network Analysis for Risk Evaluation). By allowing one to use domain knowledge as well as link knowledge, the method was very successful for pinpointing misstated accounts in our sample of general ledger data, with a significant improvement over the default heuristic in true positive rates, and a lift factor of up to 6.5 (more than twice that of the default heuristic). We also apply SNARE to the task of graph labeling in general on publicly-available datasets. We show that with only some information about the nodes themselves in a network, we get surprisingly high accuracy of labels. Not only is SNARE applicable in a wide variety of domains, but it is also robust to the choice of parameters and highly scalable-linearly with the number of edges in a graph.

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Cited By

Chen ZSun A(2024)DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World NetworkACM Transactions on Knowledge Discovery from Data10.1145/364946018:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649460
Riza LPutra ZZain MTrihutama FUtama JSamah KHerdiwijaya DNQZ RMumpuni EPriyatikanto R(2024)Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving averageInternational Journal of Data Science and Analytics10.1007/s41060-024-00535-8Online publication date: 20-Apr-2024
https://doi.org/10.1007/s41060-024-00535-8
Gu HSchreyer MMoffitt KVasarhelyi M(2023)Artificial Intelligence Co-Piloted AuditingSSRN Electronic Journal10.2139/ssrn.4444763Online publication date: 2023
https://doi.org/10.2139/ssrn.4444763
Show More Cited By

Index Terms

SNARE: a link analytic system for graph labeling and risk detection
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen ZSun A(2024)DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World NetworkACM Transactions on Knowledge Discovery from Data10.1145/364946018:6(1-20)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3649460
Riza LPutra ZZain MTrihutama FUtama JSamah KHerdiwijaya DNQZ RMumpuni EPriyatikanto R(2024)Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving averageInternational Journal of Data Science and Analytics10.1007/s41060-024-00535-8Online publication date: 20-Apr-2024
https://doi.org/10.1007/s41060-024-00535-8
Gu HSchreyer MMoffitt KVasarhelyi M(2023)Artificial Intelligence Co-Piloted AuditingSSRN Electronic Journal10.2139/ssrn.4444763Online publication date: 2023
https://doi.org/10.2139/ssrn.4444763
Pang GShen CJin Hvan den Hengel ASingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Deep Weakly-supervised Anomaly DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599302(1795-1807)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599302
Tong GShen J(2023)Financial transaction fraud detector based on imbalance learning and graph neural networkApplied Soft Computing10.1016/j.asoc.2023.110984149(110984)Online publication date: Dec-2023
https://doi.org/10.1016/j.asoc.2023.110984
Das AAjay ASaha SBhuyan M(2023)Few-Shot Anomaly Detection in Text with Deviation LearningNeural Information Processing10.1007/978-981-99-8082-6_33(425-438)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-8082-6_33
Cheng LLi BHe RYao F(2023)CenAD: Collaborative Embedding Network for Anomaly Detection with Leveraging Partially Observed AnomaliesNeural Information Processing10.1007/978-981-99-8070-3_32(418-432)Online publication date: 15-Nov-2023
https://doi.org/10.1007/978-981-99-8070-3_32
Choi JTosyali AKim BLee HJeong M(2022)A Novel Method for Identifying Competitors Using a Financial Transaction NetworkIEEE Transactions on Engineering Management10.1109/TEM.2019.293166069:4(845-860)Online publication date: Aug-2022
https://doi.org/10.1109/TEM.2019.2931660
Li ZZhao YGeng YZhao ZWang HChen WJiang HVaidya ASu LPei D(2022)Situation-Aware Multivariate Time Series Anomaly Detection Through Active Learning and Contrast VAE-Based Models in Large Distributed SystemsIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.319134140:9(2746-2765)Online publication date: Sep-2022
https://doi.org/10.1109/JSAC.2022.3191341
Yoo JKim JYoon HKim GJang CKang U(2022)Graph-based PU learning for binary and multiclass classification without class priorKnowledge and Information Systems10.1007/s10115-022-01702-864:8(2141-2169)Online publication date: 30-Jun-2022
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