Link prediction in a bipartite network using Wikipedia revision information

Yang Jui Chang, Hung-Yu Kao

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

11 Citations (Scopus)

Abstract

We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.

Original languageEnglish
Title of host publicationProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
Pages50-55
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
Duration: 2012 Nov 162012 Nov 18

Publication series

NameProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

Other

Other2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
CountryTaiwan
CityTainan
Period12-11-1612-11-18

Fingerprint

Supervised learning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Chang, Y. J., & Kao, H-Y. (2012). Link prediction in a bipartite network using Wikipedia revision information. In Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 (pp. 50-55). [6395005] (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012). https://doi.org/10.1109/TAAI.2012.49
Chang, Yang Jui ; Kao, Hung-Yu. / Link prediction in a bipartite network using Wikipedia revision information. Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012. 2012. pp. 50-55 (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012).
@inproceedings{3e9e2a4baf9b4271a3617075f1651e70,
title = "Link prediction in a bipartite network using Wikipedia revision information",
abstract = "We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.",
author = "Chang, {Yang Jui} and Hung-Yu Kao",
year = "2012",
month = "12",
day = "1",
doi = "10.1109/TAAI.2012.49",
language = "English",
isbn = "9780769549194",
series = "Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012",
pages = "50--55",
booktitle = "Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012",

}

Chang, YJ & Kao, H-Y 2012, Link prediction in a bipartite network using Wikipedia revision information. in Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012., 6395005, Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012, pp. 50-55, 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012, Tainan, Taiwan, 12-11-16. https://doi.org/10.1109/TAAI.2012.49

Link prediction in a bipartite network using Wikipedia revision information. / Chang, Yang Jui; Kao, Hung-Yu.

Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012. 2012. p. 50-55 6395005 (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012).

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

TY - GEN

T1 - Link prediction in a bipartite network using Wikipedia revision information

AU - Chang, Yang Jui

AU - Kao, Hung-Yu

PY - 2012/12/1

Y1 - 2012/12/1

N2 - We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.

AB - We consider the problem of link prediction in the bipartite network of Wikipedia. Bipartite stands for an important class in social networks, and many unipartite networks can be reinterpreted as bipartite networks when edges are modeled as vertices, such as co-authorship networks. While bipartite is the special case of general graphs, common link prediction function cannot predict the edge occurrence in bipartite graph without any specialization. In this paper, we formulate an undirected bipartite graph using the history revision information in Wikipedia. We adapt the topological features to the bipartite of Wikipedia, and apply a supervised learning approach to our link prediction formulation of the problem. We also compare the performance of link prediction model with different features.

UR - http://www.scopus.com/inward/record.url?scp=84873349121&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84873349121&partnerID=8YFLogxK

U2 - 10.1109/TAAI.2012.49

DO - 10.1109/TAAI.2012.49

M3 - Conference contribution

AN - SCOPUS:84873349121

SN - 9780769549194

T3 - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

SP - 50

EP - 55

BT - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

ER -

Chang YJ, Kao H-Y. Link prediction in a bipartite network using Wikipedia revision information. In Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012. 2012. p. 50-55. 6395005. (Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012). https://doi.org/10.1109/TAAI.2012.49