Detecting anomalous behaviors using structural properties of social networks

Yaniv Altshuler; Michael Fire; Erez Shmueli; Yuval Elovici; Alfred Bruckstein; Alex Pentland; David Lazer

doi:10.1007/978-3-642-37210-0_47

Detecting anomalous behaviors using structural properties of social networks

Yaniv Altshuler, Michael Fire, Erez Shmueli, Yuval Elovici, Alfred Bruckstein, Alex Pentland, David Lazer

Ben-Gurion University of the Negev, Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous "real world event". We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation.

Original language	English
Title of host publication	Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
Pages	433-440
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-37210-0_47
State	Published - 14 Mar 2013
Event	6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013 - Washington, DC, United States Duration: 2 Apr 2013 → 5 Apr 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7812 LNCS

Conference

Conference	6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
Country/Territory	United States
City	Washington, DC
Period	2/04/13 → 5/04/13

Keywords

Anomalies Detection
Behavior Modeling
Emergencies
Mobile Networks

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-37210-0_47

Cite this

Altshuler, Y., Fire, M., Shmueli, E., Elovici, Y., Bruckstein, A., Pentland, A., & Lazer, D. (2013). Detecting anomalous behaviors using structural properties of social networks. In Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings (pp. 433-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7812 LNCS). https://doi.org/10.1007/978-3-642-37210-0_47

Altshuler, Yaniv ; Fire, Michael ; Shmueli, Erez et al. / Detecting anomalous behaviors using structural properties of social networks. Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings. 2013. pp. 433-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detecting anomalous behaviors using structural properties of social networks",

abstract = "In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous {"}real world event{"}. We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation.",

keywords = "Anomalies Detection, Behavior Modeling, Emergencies, Mobile Networks",

author = "Yaniv Altshuler and Michael Fire and Erez Shmueli and Yuval Elovici and Alfred Bruckstein and Alex Pentland and David Lazer",

year = "2013",

month = mar,

day = "14",

doi = "10.1007/978-3-642-37210-0_47",

language = "الإنجليزيّة",

isbn = "9783642372094",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "433--440",

booktitle = "Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings",

note = "6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013 ; Conference date: 02-04-2013 Through 05-04-2013",

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Altshuler, Y, Fire, M , Shmueli, E , Elovici, Y, Bruckstein, A, Pentland, A & Lazer, D 2013, Detecting anomalous behaviors using structural properties of social networks. in Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7812 LNCS, pp. 433-440, 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013, Washington, DC, United States, 2/04/13. https://doi.org/10.1007/978-3-642-37210-0_47

Detecting anomalous behaviors using structural properties of social networks. / Altshuler, Yaniv; Fire, Michael ; Shmueli, Erez et al.
Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings. 2013. p. 433-440 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7812 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting anomalous behaviors using structural properties of social networks

AU - Altshuler, Yaniv

AU - Fire, Michael

AU - Shmueli, Erez

AU - Elovici, Yuval

AU - Bruckstein, Alfred

AU - Pentland, Alex

AU - Lazer, David

PY - 2013/3/14

Y1 - 2013/3/14

N2 - In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous "real world event". We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation.

AB - In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous "real world event". We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation.

KW - Anomalies Detection

KW - Behavior Modeling

KW - Emergencies

KW - Mobile Networks

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U2 - 10.1007/978-3-642-37210-0_47

DO - 10.1007/978-3-642-37210-0_47

M3 - منشور من مؤتمر

SN - 9783642372094

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 433

EP - 440

BT - Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings

T2 - 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013

Y2 - 2 April 2013 through 5 April 2013

ER -

Altshuler Y, Fire M , Shmueli E , Elovici Y, Bruckstein A, Pentland A et al. Detecting anomalous behaviors using structural properties of social networks. In Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings. 2013. p. 433-440. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-37210-0_47

Detecting anomalous behaviors using structural properties of social networks

Abstract

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Conference

Keywords

All Science Journal Classification (ASJC) codes

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