Finding hidden group structure in a stream of communications

J. Baumes, M. Goldberg, M. Hayvanovych, M. Magdon-Ismail, W. Wallace, M. Zaki

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

7 Citations (Scopus)

Abstract

A hidden group in a communication network is a group of individuals planning an activity over a communication medium without announcing their intentions. We develop algorithms for separating non-random planning-related communications from random background communications in a streaming model. This work extends previous results related to the identification of hidden groups in the cyclic model. The new statistical model and new algorithms do not assume the existence of a planning time-cycle in the stream of communications of a hidden group. The algorithms construct larger hidden groups by building them up from smaller ones. To illustrate our algorithms, we apply them to the Enron email corpus in order to extract the evolution of Enron's organizational structure.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages201-212
Number of pages12
Volume3975 LNCS
DOIs
Publication statusPublished - 19 Jul 2006
Externally publishedYes
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2006 - San Diego, CA, United States
Duration: 23 May 200624 May 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3975 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2006
CountryUnited States
CitySan Diego, CA
Period23/5/0624/5/06

Fingerprint

Group Structure
Communication
Planning
Communications Media
Social Identification
Electronic mail
Statistical Models
Telecommunication networks
Electronic Mail
Identification (control systems)
Streaming
Communication Networks
Statistical Model
Cycle
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Baumes, J., Goldberg, M., Hayvanovych, M., Magdon-Ismail, M., Wallace, W., & Zaki, M. (2006). Finding hidden group structure in a stream of communications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3975 LNCS, pp. 201-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3975 LNCS). https://doi.org/10.1007/11760146_18

Finding hidden group structure in a stream of communications. / Baumes, J.; Goldberg, M.; Hayvanovych, M.; Magdon-Ismail, M.; Wallace, W.; Zaki, M.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS 2006. p. 201-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3975 LNCS).

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

Baumes, J, Goldberg, M, Hayvanovych, M, Magdon-Ismail, M, Wallace, W & Zaki, M 2006, Finding hidden group structure in a stream of communications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3975 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3975 LNCS, pp. 201-212, IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, United States, 23/5/06. https://doi.org/10.1007/11760146_18
Baumes J, Goldberg M, Hayvanovych M, Magdon-Ismail M, Wallace W, Zaki M. Finding hidden group structure in a stream of communications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS. 2006. p. 201-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11760146_18
Baumes, J. ; Goldberg, M. ; Hayvanovych, M. ; Magdon-Ismail, M. ; Wallace, W. ; Zaki, M. / Finding hidden group structure in a stream of communications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS 2006. pp. 201-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{f8c10c59ac57436c9a0351552a0a2512,
title = "Finding hidden group structure in a stream of communications",
abstract = "A hidden group in a communication network is a group of individuals planning an activity over a communication medium without announcing their intentions. We develop algorithms for separating non-random planning-related communications from random background communications in a streaming model. This work extends previous results related to the identification of hidden groups in the cyclic model. The new statistical model and new algorithms do not assume the existence of a planning time-cycle in the stream of communications of a hidden group. The algorithms construct larger hidden groups by building them up from smaller ones. To illustrate our algorithms, we apply them to the Enron email corpus in order to extract the evolution of Enron's organizational structure.",
author = "J. Baumes and M. Goldberg and M. Hayvanovych and M. Magdon-Ismail and W. Wallace and M. Zaki",
year = "2006",
month = "7",
day = "19",
doi = "10.1007/11760146_18",
language = "English",
isbn = "3540344780",
volume = "3975 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "201--212",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Finding hidden group structure in a stream of communications

AU - Baumes, J.

AU - Goldberg, M.

AU - Hayvanovych, M.

AU - Magdon-Ismail, M.

AU - Wallace, W.

AU - Zaki, M.

PY - 2006/7/19

Y1 - 2006/7/19

N2 - A hidden group in a communication network is a group of individuals planning an activity over a communication medium without announcing their intentions. We develop algorithms for separating non-random planning-related communications from random background communications in a streaming model. This work extends previous results related to the identification of hidden groups in the cyclic model. The new statistical model and new algorithms do not assume the existence of a planning time-cycle in the stream of communications of a hidden group. The algorithms construct larger hidden groups by building them up from smaller ones. To illustrate our algorithms, we apply them to the Enron email corpus in order to extract the evolution of Enron's organizational structure.

AB - A hidden group in a communication network is a group of individuals planning an activity over a communication medium without announcing their intentions. We develop algorithms for separating non-random planning-related communications from random background communications in a streaming model. This work extends previous results related to the identification of hidden groups in the cyclic model. The new statistical model and new algorithms do not assume the existence of a planning time-cycle in the stream of communications of a hidden group. The algorithms construct larger hidden groups by building them up from smaller ones. To illustrate our algorithms, we apply them to the Enron email corpus in order to extract the evolution of Enron's organizational structure.

UR - http://www.scopus.com/inward/record.url?scp=33745890895&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745890895&partnerID=8YFLogxK

U2 - 10.1007/11760146_18

DO - 10.1007/11760146_18

M3 - Conference contribution

SN - 3540344780

SN - 9783540344780

VL - 3975 LNCS

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

SP - 201

EP - 212

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

ER -