By C. Lee Giles, Marc Smith, John Yen, Haizheng Zhang
This paintings constitutes the complaints of the second one overseas Workshop on Advances in Social community and research, held in Las Vegas, NV, united states in August 2008.
Read Online or Download Advances in Social Network Mining and Analysis: Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008. Revised Selected Papers ... Computer Science and General Issues) PDF
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Additional resources for Advances in Social Network Mining and Analysis: Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008. Revised Selected Papers ... Computer Science and General Issues)
We labeled users by the topic through which they were discovered. In other words, users who uploaded one of the 500 most interesting images retrieved by the query tiger, were labeled wildlife, whether or not they were interested in wildlife photography. The contacts and contacts’s contacts of the four users within this set identiﬁed as being interested in wildlife photography were also labeled wildlife. Although we did not verify that all the labeled users were indeed interested in the topic, we use these soft labels to evaluate the discovered communities.
28 R. Ghosh and K. 2 College Football We also ran our approach on the US College football data from Girvan et al. 1 The network represents the schedule of Division 1 games for the 2000 season where the vertices represent teams and the edges represent the regular season game between the two teams they connect. The teams are divided into “conferences” (or communities) containing 8 to 12 teams each. Games are more frequent between members of the same conference than members of diﬀerent conferences.
We also look at the past relationship between the bloggers who communicated. We deﬁne the history of the edge (i, j) that appeared in time cycle t to the the shortest undirected distance between i and j in the graph of the time cycle t − 1. Figure 4 presents the distribution of the edge histories of all observed edges of all time cycles. 0001 0 0 5 10 15 (a) Edge Stability 20 0 2 4 6 8 10 12 14 16 (b) Edge History Fig. 4. Edge stability: distribution of the number of weeks an edge appeared in 60% of all edges only appeared once.