Identifying and characterizing user communities on Twitter during crisis events
Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
Mining structural hole spanners through information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web
Deep Twitter diving: exploring topical groups in microblogs at scale
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
| Hi-index | 0.00 |
In many social networks, there exist two types of users that exhibit different influence and different behavior. For instance, statistics have shown that less than 1% of the Twitter users (e.g. entertainers, politicians, writers) produce 50% of its content, while the others (e.g. fans, followers, readers) have much less influence and completely different social behavior. In this paper, we define and explore a novel problem called community kernel detection in order to uncover the hidden community structure in large social networks. We discover that influential users pay closer attention to those who are more similar to them, which leads to a natural partition into different community kernels. We propose Greedy and We BA, two efficient algorithms for finding community kernels in large social networks. Greedy is based on maximum cardinality search, while We BA formalizes the problem in an optimization framework. We conduct experiments on three large social networks: Twitter, Wikipedia, and Coauthor, which show that We BA achieves an average 15%-50% performance improvement over the other state-of-the-art algorithms, and We BA is on average 6-2,000 times faster in detecting community kernels.