Towards topic following in heterogeneous information networks

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

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

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 languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
PublisherAssociation for Computing Machinery, Inc
Pages363-366
Number of pages4
ISBN (Print)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

Keywords

  • Heterogenous information networks
  • Meta path
  • Topic following

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Yang, D., Xiao, Y., Tong, H., Cui, W., & Wang, W. (2015). Towards topic following in heterogeneous information networks. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 363-366). 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; Cui, Wanyun; Wang, Wei.

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.

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

Yang, D, Xiao, Y, Tong, H, Cui, W & Wang, W 2015, Towards topic following in heterogeneous information networks. in 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 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 https://doi.org/10.1145/2808797.2809417
Yang, Deqing ; Xiao, Yanghua ; Tong, Hanghang ; Cui, Wanyun ; Wang, Wei. / 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. Association for Computing Machinery, Inc, 2015. pp. 363-366
@inproceedings{7c33379b15fb4a2e8e06a545fedb5551,
title = "Towards topic following in heterogeneous information networks",
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",
year = "2015",
month = "8",
day = "25",
doi = "10.1145/2808797.2809417",
language = "English (US)",
isbn = "9781450338547",
pages = "363--366",
booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - Towards topic following in heterogeneous information networks

AU - Yang, Deqing

AU - Xiao, Yanghua

AU - Tong, Hanghang

AU - Cui, Wanyun

AU - Wang, Wei

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.

KW - Heterogenous information networks

KW - Meta path

KW - Topic following

UR - http://www.scopus.com/inward/record.url?scp=84962480493&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962480493&partnerID=8YFLogxK

U2 - 10.1145/2808797.2809417

DO - 10.1145/2808797.2809417

M3 - Conference contribution

SN - 9781450338547

SP - 363

EP - 366

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

PB - Association for Computing Machinery, Inc

ER -