EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection

Ramnath Kumar; Shweta Yadav; Raminta Daniulaityte; Francois Lamy; Krishnaprasad Thirunarayan; Usha Lokala; Amit Sheth

doi:10.1145/3366423.3380263

EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection

Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, Amit Sheth

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

Original language	English
Title of host publication	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
Publisher	Association for Computing Machinery, Inc
Pages	1955-1965
Number of pages	11
ISBN (Electronic)	9781450370233
DOIs	https://doi.org/10.1145/3366423.3380263
State	Published - Apr 20 2020
Externally published	Yes
Event	29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China Duration: Apr 20 2020 → Apr 24 2020

Publication series

Name	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

Conference

Conference	29th International World Wide Web Conference, WWW 2020
Country/Territory	Taiwan, Province of China
City	Taipei
Period	4/20/20 → 4/24/20

ASJC Scopus Subject Areas

Computer Networks and Communications
Software

Keywords

Correlation Analysis
Darknet Market
Drug Trafficker Identification
Multi-view Learning
Stylometry
Sybil Detection

Access to Document

10.1145/3366423.3380263

Cite this

Kumar, R., Yadav, S., Daniulaityte, R., Lamy, F., Thirunarayan, K., Lokala, U., & Sheth, A. (2020). EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection. In The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (pp. 1955-1965). (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020). Association for Computing Machinery, Inc. https://doi.org/10.1145/3366423.3380263

EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection. / Kumar, Ramnath; Yadav, Shweta; Daniulaityte, Raminta et al.
The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020. Association for Computing Machinery, Inc, 2020. p. 1955-1965 (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020).

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

Kumar, R, Yadav, S, Daniulaityte, R, Lamy, F, Thirunarayan, K, Lokala, U & Sheth, A 2020, EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection. in The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020. The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, Association for Computing Machinery, Inc, pp. 1955-1965, 29th International World Wide Web Conference, WWW 2020, Taipei, Taiwan, Province of China, 4/20/20. https://doi.org/10.1145/3366423.3380263

Kumar R, Yadav S, Daniulaityte R, Lamy F, Thirunarayan K, Lokala U et al. EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection. In The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020. Association for Computing Machinery, Inc. 2020. p. 1955-1965. (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020). doi: 10.1145/3366423.3380263

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title = "EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection",

keywords = "Correlation Analysis, Darknet Market, Drug Trafficker Identification, Multi-view Learning, Stylometry, Sybil Detection",

author = "Ramnath Kumar and Shweta Yadav and Raminta Daniulaityte and Francois Lamy and Krishnaprasad Thirunarayan and Usha Lokala and Amit Sheth",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 29th International World Wide Web Conference, WWW 2020 ; Conference date: 20-04-2020 Through 24-04-2020",

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AU - Lamy, Francois

AU - Thirunarayan, Krishnaprasad

AU - Lokala, Usha

AU - Sheth, Amit

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M3 - Conference contribution

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EDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this