ECE Researchers Investigate Social Networks in Award-Winning Paper
The award-winning paper by Xu and Eun entitled, “Modeling Time-Sensitive Information Diffusion in Online Social Networks” focused on the complex issue of how to predict how many users will forward or comment on information posted in online social networks.
Congratulations to ECE PhD student Xin Xu and Associate Professor Do Young Eun – the Best Paper Award winners at the IEEE International Workshop on Network Science for Communication Networks (NetSciCom), 2015, part of IEEE Infocom, Hong Kong.
The goal of NetSciCom was to a provide a forum where a diverse group of researchers – from engineers to behavioral scientists – could meet and exchange ideas leading to deeper insights into the design of future robust communication networks.
The award-winning paper by Xu and Eun was entitled, “Modeling Time-Sensitive Information Diffusion in Online Social Networks.” Their focus was on the complex issue of how to predict how many users will forward or comment on information posted in online social networks. After a piece of information is released, will it spread to the entire network or reach only a small population of users?
As online social networks – such as Facebook, Twitter, and Microblog – have exploded in popularity, understanding and modeling the dynamics of how information spreads over social networks has become an important research problem.
According to Eun, the traditional approach to this kind of study is the susceptible-infected (SI) model – a classical model in epidemiology dating back to the early 1900’s. Eun says, “Our work hinges on a simple yet crucial observation that, in contrast to the infectious disease or virus, the information or rumor in online social networks tends to lose its original charm and becomes less infectious over time…In particular, we have observed out of real data traces that the infection rate for information diffusion decays as a power-law. Our work will be useful in a number of important problems, including how to efficiently identify the likely source of the on-going rumor in online social networks and how to best expedite or slow down such spreading process with a given limited budget.”