Download Advances in Social Network Mining and Analysis: Second by C. Lee Giles, Marc Smith, John Yen, Haizheng Zhang PDF
By C. Lee Giles, Marc Smith, John Yen, Haizheng Zhang
This paintings constitutes the court cases of the second one foreign Workshop on Advances in Social community and research, held in Las Vegas, NV, united states in August 2008.
Read 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 info 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)
70 which is stable from week to week. This value was computed using maximum likelihood method described in  and Matlab code made available by Aaron J. Clauset. To evaluate the dynamic in the observed communication we consider the change in the set of links or edges from one week to another. Figure 4 shows the distribution of number of weeks a particular pair of bloggers communicated. It is evident from this plot that the vast majority of communication does not re-occur. Yet, some links reappear every week.
E. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics.
This indeed may be true, but whether the nodes in the network is to be divided into distinct communities or probabilities with which each node belongs to community is to be discovered, really depends on the speciﬁc application. By this, we mean that if the application we are interested in is ﬁnding the natural communities say in the karate club data, and if we use a probabilistic method (say ), we would be assigning the nodes into groups into which their probability of belonging is the highest, and the communities thus formed do not necessarily portray the division of the network into natural communities observed.