Mining the best observational window to model social phenomena

Chao Yan, Zhijun Yin, Stanley Xiang, You Chen, Yevgeniy Vorobeychik, Daniel Fabbri, Abel N Kho, David Liebovitz, Bradley Malin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The structure and behavior of organizations can be learned by mining the event logs of the information systems they manage. This supports numerous applications, such as inferring the structure of social relations, uncovering implicit workflows, and detecting illicit behavior. However, to date, no clear guidelines regarding how to select an appropriate time period to perform organizational modeling have been articulated. This is a significant concern because an inaccurately defined period can lead to incorrect models and poor performance in data-driven applications. In this paper, we introduce a data-driven approach to infer the optimal time period for organizational modeling. Our approach 1) represents the system as a social network, 2) decomposes it into its respective principal components, and 3) optimizes the signal-to-noise ratio over varying temporal observation windows. In doing so, we minimize the variance in the organizational structure while maximizing its patterns. We assess the capability of this approach using an anomaly detection scenario, which is based on the patterns learned from the interactions documented in audit logs. The classification performance of two known algorithms is investigated over a range of time periods in two representative datasets. First, we use the electronic health record access logs from Northwestern Memorial Hospital to demonstrate that our framework detects a period that coincides with the optimal performance of the anomaly detection algorithms. Second, we assess the generalizability of the framework through an analysis with a less clearly defined organization, in the form of the social network inferred from the DBLP co-authorship dataset. The results with this data further illustrate that our framework can discover the optimal time period in the context of a more loosely organized group.

Original languageEnglish (US)
Title of host publicationProceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-55
Number of pages10
ISBN (Electronic)9781538695029
DOIs
StatePublished - Nov 15 2018
Event4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018 - Philadelphia, United States
Duration: Oct 18 2018Oct 20 2018

Publication series

NameProceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018

Other

Other4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018
CountryUnited States
CityPhiladelphia
Period10/18/1810/20/18

Fingerprint

Signal to noise ratio
Information systems
Health
Social model
Social networks
Anomaly detection
Organizational modeling
Interaction
Principal components
Audit
Co-authorship
Generalizability
Organizational structure
Electronic health record
Scenarios
Social relations

Keywords

  • Anomaly detection
  • Data Mining
  • Organizational modeling
  • Temporal optimization

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

Cite this

Yan, C., Yin, Z., Xiang, S., Chen, Y., Vorobeychik, Y., Fabbri, D., ... Malin, B. (2018). Mining the best observational window to model social phenomena. In Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018 (pp. 46-55). [8537816] (Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIC.2018.00-41
Yan, Chao ; Yin, Zhijun ; Xiang, Stanley ; Chen, You ; Vorobeychik, Yevgeniy ; Fabbri, Daniel ; Kho, Abel N ; Liebovitz, David ; Malin, Bradley. / Mining the best observational window to model social phenomena. Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 46-55 (Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018).
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Yan, C, Yin, Z, Xiang, S, Chen, Y, Vorobeychik, Y, Fabbri, D, Kho, AN, Liebovitz, D & Malin, B 2018, Mining the best observational window to model social phenomena. in Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018., 8537816, Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 46-55, 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018, Philadelphia, United States, 10/18/18. https://doi.org/10.1109/CIC.2018.00-41

Mining the best observational window to model social phenomena. / Yan, Chao; Yin, Zhijun; Xiang, Stanley; Chen, You; Vorobeychik, Yevgeniy; Fabbri, Daniel; Kho, Abel N; Liebovitz, David; Malin, Bradley.

Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 46-55 8537816 (Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Yan, Chao

AU - Yin, Zhijun

AU - Xiang, Stanley

AU - Chen, You

AU - Vorobeychik, Yevgeniy

AU - Fabbri, Daniel

AU - Kho, Abel N

AU - Liebovitz, David

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N2 - The structure and behavior of organizations can be learned by mining the event logs of the information systems they manage. This supports numerous applications, such as inferring the structure of social relations, uncovering implicit workflows, and detecting illicit behavior. However, to date, no clear guidelines regarding how to select an appropriate time period to perform organizational modeling have been articulated. This is a significant concern because an inaccurately defined period can lead to incorrect models and poor performance in data-driven applications. In this paper, we introduce a data-driven approach to infer the optimal time period for organizational modeling. Our approach 1) represents the system as a social network, 2) decomposes it into its respective principal components, and 3) optimizes the signal-to-noise ratio over varying temporal observation windows. In doing so, we minimize the variance in the organizational structure while maximizing its patterns. We assess the capability of this approach using an anomaly detection scenario, which is based on the patterns learned from the interactions documented in audit logs. The classification performance of two known algorithms is investigated over a range of time periods in two representative datasets. First, we use the electronic health record access logs from Northwestern Memorial Hospital to demonstrate that our framework detects a period that coincides with the optimal performance of the anomaly detection algorithms. Second, we assess the generalizability of the framework through an analysis with a less clearly defined organization, in the form of the social network inferred from the DBLP co-authorship dataset. The results with this data further illustrate that our framework can discover the optimal time period in the context of a more loosely organized group.

AB - The structure and behavior of organizations can be learned by mining the event logs of the information systems they manage. This supports numerous applications, such as inferring the structure of social relations, uncovering implicit workflows, and detecting illicit behavior. However, to date, no clear guidelines regarding how to select an appropriate time period to perform organizational modeling have been articulated. This is a significant concern because an inaccurately defined period can lead to incorrect models and poor performance in data-driven applications. In this paper, we introduce a data-driven approach to infer the optimal time period for organizational modeling. Our approach 1) represents the system as a social network, 2) decomposes it into its respective principal components, and 3) optimizes the signal-to-noise ratio over varying temporal observation windows. In doing so, we minimize the variance in the organizational structure while maximizing its patterns. We assess the capability of this approach using an anomaly detection scenario, which is based on the patterns learned from the interactions documented in audit logs. The classification performance of two known algorithms is investigated over a range of time periods in two representative datasets. First, we use the electronic health record access logs from Northwestern Memorial Hospital to demonstrate that our framework detects a period that coincides with the optimal performance of the anomaly detection algorithms. Second, we assess the generalizability of the framework through an analysis with a less clearly defined organization, in the form of the social network inferred from the DBLP co-authorship dataset. The results with this data further illustrate that our framework can discover the optimal time period in the context of a more loosely organized group.

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Yan C, Yin Z, Xiang S, Chen Y, Vorobeychik Y, Fabbri D et al. Mining the best observational window to model social phenomena. In Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 46-55. 8537816. (Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018). https://doi.org/10.1109/CIC.2018.00-41