Using Topic Discovery to Segment Large Communication Graphs for Social Network Analysis Export

@inproceedings{citeulike:2865336, abstract = {The application of social network analysis to graphs found in the World Wide Web and the Internet has received increasing attention in recent years. Networks as diverse as those generated by e-mail communication, instant messaging, link structure in the Internet as well as citation and collaboration networks have all been treated with this method. So far these analyses solely utilize graph structure. There is, however, another source of information available in messaging corpora, namely content. We propose to apply the field of content analysis to the process of social network analysis. By extracting relevant and cohesive sub-networks from massive graphs, we obtain information on the actors contained in such sub-networks to a much firmer degree than before.}, author = {Viermetz, Maximilian and Skubacz, Michal}, booktitle = {Web Intelligence, IEEE/WIC/ACM International Conference on}, citeulike-article-id = {2865336}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/WI.2007.90}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4427072}, doi = {10.1109/WI.2007.90}, journal = {Web Intelligence, IEEE/WIC/ACM International Conference on}, keywords = {clustering, communication, community, graphs, semantics}, pages = {95--99}, posted-at = {2008-06-05 13:49:11}, priority = {0}, title = {Using Topic Discovery to Segment Large Communication Graphs for Social Network Analysis}, url = {http://dx.doi.org/10.1109/WI.2007.90}, year = {2007} }

TY - CONF ID - citeulike:2865336 L3 - citeulike-article-id:2865336 N2 - The application of social network analysis to graphs found in the World Wide Web and the Internet has received increasing attention in recent years. Networks as diverse as those generated by e-mail communication, instant messaging, link structure in the Internet as well as citation and collaboration networks have all been treated with this method. So far these analyses solely utilize graph structure. There is, however, another source of information available in messaging corpora, namely content. We propose to apply the field of content analysis to the process of social network analysis. By extracting relevant and cohesive sub-networks from massive graphs, we obtain information on the actors contained in such sub-networks to a much firmer degree than before. JF - Web Intelligence, IEEE/WIC/ACM International Conference on EP - 99 TI - Using Topic Discovery to Segment Large Communication Graphs for Social Network Analysis T2 - Web Intelligence, IEEE/WIC/ACM International Conference on SP - 95 KW - clustering KW - communication KW - community KW - graphs KW - semantics AU - Viermetz, Maximilian AU - Skubacz, Michal PY - 2007/// UR - http://dx.doi.org/10.1109/WI.2007.90 ER -