Online User Activities Discovery Based on Time Dependent Data

Network evolution is a hot research topic especially when social networking has become an important Web application. The access histories of Web users which contain the users traces´ on a social network have not been considered useful data. However, they may reveal more about the network´s connectedness if the history´s time-sensitive characteristic is analyzed and studied. In this paper, we model the user´s daily activities in a time series model to reflect the dynamic nature of a social network due to various user behavior patterns over a period of time. We begin to study the activity pattern for a single user. We then expand that study over the whole network. Through the model, we can quantitatively analyze the user´s contribution to the social network and predict the user´s response when there is a new action by another user.