%0 Conference Paper %1 Zhang_2007 %A Zhang, Jun %A Ackerman, Mark S. %A Adamic, Lada %B WWW '07: Proceedings of the 16th international conference on World Wide Web %C New York, NY, USA %D 2007 %I ACM Press %K community expertise %P 221--230 %R http://doi.acm.org/10.1145/1242572.1242603 %T Expertise networks in online communities: structure and algorithms %U http://portal.acm.org/citation.cfm?id=1242603&coll=GUIDE&dl=ACM&CFID=21633871&CFTOKEN=81037701 %X Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities. %@ 978-1-59593-654-7

@inproceedings{Zhang_2007, abstract = {Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.}, added-at = {2007-07-30T17:24:47.000+0200}, address = {New York, NY, USA}, author = {Zhang, Jun and Ackerman, Mark S. and Adamic, Lada}, biburl = {https://www.bibsonomy.org/bibtex/2fd1f9922d96f6920f93e3f306ffdf519/tobold}, booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web}, description = {Expertise networks in online communities}, doi = {http://doi.acm.org/10.1145/1242572.1242603}, interhash = {e606f9c93602096c1c154d9ccfafe0d4}, intrahash = {fd1f9922d96f6920f93e3f306ffdf519}, isbn = {978-1-59593-654-7}, keywords = {community expertise}, location = {Banff, Alberta, Canada}, pages = {221--230}, publisher = {ACM Press}, timestamp = {2008-03-05T12:02:09.000+0100}, title = {Expertise networks in online communities: structure and algorithms}, url = {http://portal.acm.org/citation.cfm?id=1242603&coll=GUIDE&dl=ACM&CFID=21633871&CFTOKEN=81037701}, year = 2007 }