Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
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With the effect of word-of-the-mouth, trends in social networks are now playing a significant role in shaping people's lives. Predicting dynamic trends is an important problem with many useful applications. There are three dynamic characteristics of a trend that should be captured by a trend model: intensity, coverage and duration. However, existing approaches on the information diffusion are not capable of capturing these three characteristics. In this paper, we study the problem of predicting dynamic trends in social networks. We first define related concepts to quantify the dynamic characteristics of trends in social networks, and formalize the problem of trend prediction. We then propose a Dynamic Activeness (DA) model based on the novel concept of activeness, and design a trend prediction algorithm using the DA model. We examine the prediction algorithm on the DBLP network, and show that it is more accurate than state-of-the-art approaches.