This paper proposes that social network data should be assumed public but treated private. Assuming this rather confusing requirement means that anonymity models such as kanonymity...
The spread of influence among individuals in a social network can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between vari...
Dan Cosley, Daniel P. Huttenlocher, Jon M. Kleinbe...
The current methods used to mine and analyze temporal social network data make two assumptions: all edges have the same strength, and all parameters are time-homogeneous. We show ...
This paper reports on a mechanism to identify temporal spatial trends in social networks. The trends of interest are defined in terms of the occurrence frequency of time stamped p...
Puteri N. E. Nohuddin, Rob Christley, Frans Coenen...
The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology users may not realize is that the information they provide...
Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of resea...
Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With som...