N13-1041 large amount of interest in online social network analysis . Most existing work on social
P12-2032 predicting the dominance relation using social network analysis metric is simple . We calculate
D12-1006 Social Networks Most of the work on social networks analysis has only focused on positive
P05-2018 Graph theory has been used in social network analysis to identify those actors who
N07-1013 , their cosine similarity . In social network analysis one can create a directed graph
N07-1013 effectiveness in text summarization and social network analysis . As future work , one direction
N07-1013 results on text summarization and social network analysis in Section 3 . 2 The GRASSHOPPER
N09-3002 references are key to many tasks , e.g. social network analysis , this work focuses on person
P12-2032 predictor for hierarchy based on social network analysis ( SNA ) , namely degree centrality
P05-2018 present the approach of using social network analysis technique to find terms , specifically
J10-3004 and Acar et al. ( 2005 ) perform social network analysis , extracting groups of speakers
P10-3009 tasks like new word detection , social network analysis , user behavior analysis , and
P10-1115 extension of PLSA for incorporating social network analysis ( Mei et al. , 2008a ) but our
N12-1057 field , several studies have used Social Network Analysis ( e.g. , ( Diesner and Carley
D10-1121 efforts that go beyond standard social network analysis that is based on positve links
N10-1017 Grady and Schwartz , 2006 ) . In social network analysis , Liben - Nowell an d Kleinberg
P14-1141 Jeong et al. , 2009 ) , and using social network analysis to improve message classification
P12-2067 language processing ( NLP ) and social network analysis ( SNA ) . We propose a new task
N07-1013 collection summarization , and social network analysis for national security and business
D08-1054 analysis ( Dietz et al. , 2007 ) , social network analysis ( Mei et al. , 2008 ) , and so
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