OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Bringing PageRank to the citation analysis
Information Processing and Management: an International Journal
International Journal of Mobile Communications
Categorization of web pages - Performance enhancement to search engine
Knowledge-Based Systems
PageRank for ranking authors in co-citation networks
Journal of the American Society for Information Science and Technology
Topic initiator detection on the world wide web
Proceedings of the 19th international conference on World wide web
Optimal Windows for Aggregating Ratings in Electronic Marketplaces
Management Science
SMS-based web search for low-end mobile devices
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Measuring message propagation and social influence on Twitter.com
SocInfo'10 Proceedings of the Second international conference on Social informatics
Word AdHoc Network: Using Google Core Distance to extract the most relevant information
Knowledge-Based Systems
PageRank: standing on the shoulders of giants
Communications of the ACM
Evaluation and recommendation methods based on graph model
BI'11 Proceedings of the 2011 international conference on Brain informatics
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Identifying short message services (SMSs) seed users helps to discover the information's originals and transmission paths. A tree-network model was proposed to depict the characteristics of SMS seed users who have such three features as ''ahead of time'', ''mass texting'' and ''numerous retransmissions''. For acquiring the established network model's width and depth, a clustering algorithm based on density was adopted and a recursion algorithm was designed to solve such problems. An objective, comprehensive and scale-free evaluation function was further presented to rank the potential seed users by using the width and the depth obtained above. Furthermore, the model's empirical analysis was made based on part of the Shenzhen's cell phone SMS data in February of 2012. The model is effective and applicable as a powerful tool to solve the SMS seed users' mining problem.