State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation. While powerful, these methods ...
Minas Gjoka, Carter T. Butts, Maciej Kurant, Athin...
We present a methodology for enhancing the delivery of usergenerated content in online social networks. To this end, we first regularize the social graph via node capacity and li...
This paper presents a novel social media summarization framework. Summarizing media created and shared in large scale online social networks unfolds challenging research problems....
In this work, we investigate the use of online or “crawling” algorithms to sample large social networks in order to determine the most influential or important individuals wit...
We present a framework for automatically summarizing social group activity over time. The problem is important in understanding large scale online social networks, which have dive...