The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events.Existing work relies on snapshot data and misses out some key functions of networks. Here, we study contact network evolution where no snapshot data are available.In spite of the challenge, this study demonstrates how contact networks can be used to predict and control infectious disease epidemics.We first model structural changes in contact networks during the 2009 influenza pandemic in Hong Kong, and then present a probabilistic framework to address it, aiming to answer when and how the underlying structure changes, utilizing multiple data sources including demographic data, and epidemic surveillance data.The efficacy and public health utility of the method are demonstrated using both synthetic and real data.

Individual mood is important for physical and emotional well-being, creativity and working memory. However, due to the lack of long-term real tracking daily data in individual level, most current works focus their efforts on population level and short-term small group. An ignored yet important task is to find the sentiment spreading mechanism in individual level from their daily behavior data. This paper studies this task by raising the following fundamental and summarization question, being not sufficiently answered by the literature so far:Given a social network, how the sentiment spread? The current individual-level network spreading models always assume one can infect others only when he/she has been infected. Considering the negative emotion spreading characters in individual level, we loose this assumption, and give an individual negative emotion spreading model. In this paper, we propose a Graph-Coupled Hidden Markov Sentiment Model for modeling the propagation of infectious negative sentiment locally within a social network. Taking the MIT Social Evolution dataset as an example, the experimental results verify the efficacy of our techniques on real-world data.