Towards topic following in heterogeneous information networks

Deqing Yang; Yanghua Xiao; Hanghang Tong; Wanyun Cui; Wei Wang

doi:10.1145/2808797.2809417

Towards topic following in heterogeneous information networks

Deqing Yang, Yanghua Xiao, Hanghang Tong, Wanyun Cui, Wei Wang

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

2 Scopus citations

Abstract

Who are the best targets to receive a call-for-paper or call-for-participation? What kind of topics should we propose for a workshop or a special issue of next year? Precisely predicting author's topic following behavior, i.e., publishing papers of a certain research topic in future, is essential to answer these questions. In this paper, we aim to model and predict author's topic following behavior in a heterogeneous information network. The heart of our methodology is to evaluate the author-author similarity through informative meta paths in the network. The models we propose in this paper can predict not only whether a given author will follow a certain topic but also the topic distribution over all publications in the next year. Extensive experimental evaluations justify that the prediction performance of our approach outperforms the existing approaches across various topics.

Original language	English (US)
Title of host publication	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Editors	Jian Pei, Jie Tang, Fabrizio Silvestri
Publisher	Association for Computing Machinery, Inc
Pages	363-366
Number of pages	4
ISBN (Electronic)	9781450338547
DOIs	https://doi.org/10.1145/2808797.2809417
State	Published - Aug 25 2015
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: Aug 25 2015 → Aug 28 2015

Publication series

Name	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

Other	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/Territory	France
City	Paris
Period	8/25/15 → 8/28/15

Keywords

Heterogenous information networks
Meta path
Topic following

ASJC Scopus subject areas

Computer Science Applications
Computer Networks and Communications

Access to Document

10.1145/2808797.2809417

Cite this

Yang, D., Xiao, Y., Tong, H., Cui, W., & Wang, W. (2015). Towards topic following in heterogeneous information networks. In J. Pei, J. Tang, & F. Silvestri (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 363-366). (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2809417

Towards topic following in heterogeneous information networks. / Yang, Deqing; Xiao, Yanghua; Tong, Hanghang et al.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. ed. / Jian Pei; Jie Tang; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. p. 363-366 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

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

Yang, D, Xiao, Y, Tong, H, Cui, W & Wang, W 2015, Towards topic following in heterogeneous information networks. in J Pei, J Tang & F Silvestri (eds), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Association for Computing Machinery, Inc, pp. 363-366, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2809417

Yang D, Xiao Y, Tong H, Cui W, Wang W. Towards topic following in heterogeneous information networks. In Pei J, Tang J, Silvestri F, editors, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 363-366. (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). doi: 10.1145/2808797.2809417

Yang, Deqing ; Xiao, Yanghua ; Tong, Hanghang et al. / Towards topic following in heterogeneous information networks. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. editor / Jian Pei ; Jie Tang ; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. pp. 363-366 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

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abstract = "Who are the best targets to receive a call-for-paper or call-for-participation? What kind of topics should we propose for a workshop or a special issue of next year? Precisely predicting author's topic following behavior, i.e., publishing papers of a certain research topic in future, is essential to answer these questions. In this paper, we aim to model and predict author's topic following behavior in a heterogeneous information network. The heart of our methodology is to evaluate the author-author similarity through informative meta paths in the network. The models we propose in this paper can predict not only whether a given author will follow a certain topic but also the topic distribution over all publications in the next year. Extensive experimental evaluations justify that the prediction performance of our approach outperforms the existing approaches across various topics.",

keywords = "Heterogenous information networks, Meta path, Topic following",

author = "Deqing Yang and Yanghua Xiao and Hanghang Tong and Wanyun Cui and Wei Wang",

note = "Funding Information: This paper was supported by the National 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).; IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

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AU - Cui, Wanyun

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N1 - Funding Information: This paper was supported by the National 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 - 2015/8/25

Y1 - 2015/8/25

N2 - Who are the best targets to receive a call-for-paper or call-for-participation? What kind of topics should we propose for a workshop or a special issue of next year? Precisely predicting author's topic following behavior, i.e., publishing papers of a certain research topic in future, is essential to answer these questions. In this paper, we aim to model and predict author's topic following behavior in a heterogeneous information network. The heart of our methodology is to evaluate the author-author similarity through informative meta paths in the network. The models we propose in this paper can predict not only whether a given author will follow a certain topic but also the topic distribution over all publications in the next year. Extensive experimental evaluations justify that the prediction performance of our approach outperforms the existing approaches across various topics.

AB - Who are the best targets to receive a call-for-paper or call-for-participation? What kind of topics should we propose for a workshop or a special issue of next year? Precisely predicting author's topic following behavior, i.e., publishing papers of a certain research topic in future, is essential to answer these questions. In this paper, we aim to model and predict author's topic following behavior in a heterogeneous information network. The heart of our methodology is to evaluate the author-author similarity through informative meta paths in the network. The models we propose in this paper can predict not only whether a given author will follow a certain topic but also the topic distribution over all publications in the next year. Extensive experimental evaluations justify that the prediction performance of our approach outperforms the existing approaches across various topics.

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Towards topic following in heterogeneous information networks

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Keywords

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