A probability based subnet selection method for hot event detection in sina weibo microblogging

Microblogging has become a popular means of communication and information diffusion. Due to the huge amount of microblogs generated daily, the communication and computing costs required for real hot event detection is a big challenge. Choosing a small subnet of nodes to detect events has received increasing research interests in recent years. But the previous methods manage to select nodes to cover all the events including less popular events in sample datasets under the limited subnet size, which cause a big difference of event detection ratio between sample events and online real events in microblogs. In this paper we propose a new subnet nodes selection scheme based on the event detection ratio and nodes´ events participation probabilities. Under the requirement of average event detection ratio, we prefer to choose the nodes who are active in propagating hot events than the nodes who participate in the less popular events. And we take dynamic programming to accelerate the computing. The experimental results show that our proposed method has a better performance.