Which topic will you follow?

Deqing Yang; Yanghua Xiao; Bo Xu; Hanghang Tong; Wei Wang; Sheng Huang

doi:10.1007/978-3-642-33486-3_38

Which topic will you follow?

Deqing Yang, Yanghua Xiao, Bo Xu, Hanghang Tong, Wei Wang, Sheng Huang

Arizona State University

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

4 Scopus citations

Abstract

Who are the most appropriate candidates to receive a call-for-paper or call-for-participation? What session topics should we propose for a conference of next year? To answer these questions, we need to precisely predict research topics of authors. In this paper, we build a MLR (Multiple Logistic Regression) model to predict the topic-following behavior of an author. By empirical studies, we find that social influence and homophily are two fundamental driving forces of topic diffusion in SCN (Scientific Collaboration Network). Hence, we build the model upon the explanatory variables representing above two driving forces. Extensive experimental results show that our model can consistently achieves good predicting performance. Such results are independent of the tested topics and significantly better than that of state-of-the-art competitor.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages	597-612
Number of pages	16
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-33486-3_38
State	Published - 2012
Event	2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom Duration: Sep 24 2012 → Sep 28 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7524 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
Country/Territory	United Kingdom
City	Bristol
Period	9/24/12 → 9/28/12

Keywords

SCN
homophily
social influence
topic-following

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-33486-3_38

Cite this

Yang, D., Xiao, Y., Xu, B., Tong, H., Wang, W., & Huang, S. (2012). Which topic will you follow? In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings (PART 2 ed., pp. 597-612). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7524 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-33486-3_38

Which topic will you follow? / Yang, Deqing; Xiao, Yanghua; Xu, Bo et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings. PART 2. ed. 2012. p. 597-612 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7524 LNAI, No. PART 2).

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

Yang, D, Xiao, Y, Xu, B, Tong, H, Wang, W & Huang, S 2012, Which topic will you follow? in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7524 LNAI, pp. 597-612, 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012, Bristol, United Kingdom, 9/24/12. https://doi.org/10.1007/978-3-642-33486-3_38

Yang D, Xiao Y, Xu B, Tong H, Wang W, Huang S. Which topic will you follow? In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings. PART 2 ed. 2012. p. 597-612. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-33486-3_38

@inproceedings{b39e6baaaa5a4cf983794ad77f566345,

title = "Which topic will you follow?",

abstract = "Who are the most appropriate candidates to receive a call-for-paper or call-for-participation? What session topics should we propose for a conference of next year? To answer these questions, we need to precisely predict research topics of authors. In this paper, we build a MLR (Multiple Logistic Regression) model to predict the topic-following behavior of an author. By empirical studies, we find that social influence and homophily are two fundamental driving forces of topic diffusion in SCN (Scientific Collaboration Network). Hence, we build the model upon the explanatory variables representing above two driving forces. Extensive experimental results show that our model can consistently achieves good predicting performance. Such results are independent of the tested topics and significantly better than that of state-of-the-art competitor.",

keywords = "SCN, homophily, social influence, topic-following",

author = "Deqing Yang and Yanghua Xiao and Bo Xu and Hanghang Tong and Wei Wang and Sheng Huang",

note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 ; Conference date: 24-09-2012 Through 28-09-2012",

year = "2012",

doi = "10.1007/978-3-642-33486-3_38",

language = "English (US)",

isbn = "9783642334856",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "597--612",

booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings",