Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Complexity - Understanding Complex Systems: Part II
On six degrees of separation in DBLP-DB and more
ACM SIGMOD Record
A coupled HMM approach to video-realistic speech animation
Pattern Recognition
DBconnect: mining research community on DBLP data
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
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Scientific communities around the world are increasingly paying more attention to collaborative networks to ensure they remain competitive, the Computer Science (CS) community is not an exception. Discovering collaboration opportunities is a challenging problem in social networks. Traditional social network analysis allows us to observe which authors are already collaborating, how often they are related to each other, and how many intermediaries exist between two authors. In order to discover the potential collaboration among Mexican CS scholars we built a social network, containing data from 1960 to 2008. We propose to use a clustering algorithm and social network analysis to identify scholars that would be advisable to collaborate. The idea is to identify clusters consisting of authors who are completely disconnected but with opportunities of collaborating given their common research areas. After having clustered the initial social network we built, we analyze the collaboration networks of each cluster to discover new collaboration opportunities based on the conferences where the authors have published. Our analysis was made based on the large-scale DBLP bibliography and the census of Mexican scholars made by REMIDEC.