Cross-Site Virtual Social Network Construction

Chenhao Xie; Deqing Yang; Jingrui He; Yanghua Xiao

doi:10.1109/ICDMW.2015.98

Cross-Site Virtual Social Network Construction

Chenhao Xie, Deqing Yang, Jingrui He, Yanghua Xiao

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

Abstract

Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.

Original language	English (US)
Title of host publication	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Editors	Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1660-1663
Number of pages	4
ISBN (Electronic)	9781467384926
DOIs	https://doi.org/10.1109/ICDMW.2015.98
State	Published - Jan 29 2016
Event	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015

Publication series

Name	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country	United States
City	Atlantic City
Period	11/14/15 → 11/17/15

Keywords

Graph propagation
cross-site
semantic matching
virtual social network

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications

Access to Document

10.1109/ICDMW.2015.98

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Xie, C., Yang, D., He, J., & Xiao, Y. (2016). Cross-Site Virtual Social Network Construction. In X. Wu, A. Tuzhilin, H. Xiong, J. G. Dy, C. Aggarwal, Z-H. Zhou, & P. Cui (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 1660-1663). [7395882] (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2015.98

Cross-Site Virtual Social Network Construction. / Xie, Chenhao; Yang, Deqing; He, Jingrui; Xiao, Yanghua.

Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1660-1663 7395882 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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

Xie, C, Yang, D, He, J & Xiao, Y 2016, Cross-Site Virtual Social Network Construction. in X Wu, A Tuzhilin, H Xiong, JG Dy, C Aggarwal, Z-H Zhou & P Cui (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015., 7395882, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1660-1663, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDMW.2015.98

Xie C, Yang D, He J, Xiao Y. Cross-Site Virtual Social Network Construction. In Wu X, Tuzhilin A, Xiong H, Dy JG, Aggarwal C, Zhou Z-H, Cui P, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1660-1663. 7395882. (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). https://doi.org/10.1109/ICDMW.2015.98

Xie, Chenhao ; Yang, Deqing ; He, Jingrui ; Xiao, Yanghua. / Cross-Site Virtual Social Network Construction. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. editor / Xindong Wu ; Alexander Tuzhilin ; Hui Xiong ; Jennifer G. Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1660-1663 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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title = "Cross-Site Virtual Social Network Construction",

abstract = "Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.",

keywords = "Graph propagation, cross-site, semantic matching, virtual social network",

author = "Chenhao Xie and Deqing Yang and Jingrui He and Yanghua Xiao",

note = "Funding Information: This demo was supported by NSFC (No.61472085, 61171132, 61033010), by National Key Basic Research Program of China under No.2015CB358800, by Basic research project of Shanghai science and technology innovation action plan under No.15JC1400900, and by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300).; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

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N1 - Funding Information: This demo was supported by NSFC (No.61472085, 61171132, 61033010), by National Key Basic Research Program of China under No.2015CB358800, by Basic research project of Shanghai science and technology innovation action plan under No.15JC1400900, and by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300).

PY - 2016/1/29

Y1 - 2016/1/29

N2 - Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.

AB - Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.

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KW - semantic matching

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Cross-Site Virtual Social Network Construction

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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