On finding the strongly connected components in a directed graph
Information Processing Letters
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering large dense subgraphs in massive graphs
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Understanding online social network usage from a network perspective
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
An Efficient Algorithm for Solving Pseudo Clique Enumeration Problem
Algorithmica - Special Issue: Algorithms and Computation; Guest Editor: Takeshi Tokuyama
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Assessing and ranking structural correlations in graphs
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Tag-based User Topic Discovery Using Twitter Lists
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
A method for pinpoint clustering of web pages with pseudo-clique search
Proceedings of the 2005 international conference on Federation over the Web
| Hi-index | 0.00 |
User community recognition in social media services is important to identify hot topics or users' interests and concerns in a timely way when a disaster has occurred. In microblogging services, many short messages are posted every day and some of them represent replies or forwarded messages between users. We extract such conversational messages to link the users as a user network and regard the strongly-connected components in the network as indicators of user communities. However, using all of the microblog data for user community extraction is too costly and requires too much storage space when decomposing strongly-connected components. In contrast, using sampled data may miss some user connections and thus divide one user community into pieces. In this paper, we propose a method for user community reconstruction using the lexical similarity of the messages and the user's link information between separate communities.