Extracting information from large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities within the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.
%0 Conference Paper
%1 1348558
%A Zaiane, Osmar R.
%A Chen, Jiyang
%A Goebel, Randy
%B WebKDD/SNA-KDD '07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
%C New York, NY, USA
%D 2007
%I ACM
%K 2007 analysis collaboration community database detection mining relation
%P 74--81
%R http://doi.acm.org/10.1145/1348549.1348558
%T DBconnect: mining research community on DBLP data
%U http://portal.acm.org/citation.cfm?id=1348558&coll=GUIDE&dl=GUIDE&CFID=17695029&CFTOKEN=22803053&ret=1#Fulltext
%X Extracting information from large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities within the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.
%@ 978-1-59593-848-0
@inproceedings{1348558,
abstract = {Extracting information from large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities within the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.},
added-at = {2009-01-14T10:52:18.000+0100},
address = {New York, NY, USA},
author = {Zaiane, Osmar R. and Chen, Jiyang and Goebel, Randy},
biburl = {https://www.bibsonomy.org/bibtex/2913842df65b7827774a8041c9bdce9b6/anneba},
booktitle = {WebKDD/SNA-KDD '07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis},
description = {DBconnect},
doi = {http://doi.acm.org/10.1145/1348549.1348558},
interhash = {baec3eb11b9ac359c0466d3c17520ca5},
intrahash = {913842df65b7827774a8041c9bdce9b6},
isbn = {978-1-59593-848-0},
keywords = {2007 analysis collaboration community database detection mining relation},
location = {San Jose, California},
pages = {74--81},
publisher = {ACM},
timestamp = {2009-01-14T10:52:47.000+0100},
title = {DBconnect: mining research community on DBLP data},
url = {http://portal.acm.org/citation.cfm?id=1348558&coll=GUIDE&dl=GUIDE&CFID=17695029&CFTOKEN=22803053&ret=1#Fulltext},
year = 2007
}