摘要：One of the methodological and logistic problems of network research is the challenge of big data. Dynamics and network qualities are not as easy to extrapolate as averages and distributions. The numbers are huge, and traditional sampling doesn’t solve the problem. What if, by using a small sub–group of the members of a population, we could understand the nature of the network connecting them? For instance, what if we could draw network analysis conclusions, such as predicting the outbreak and evolution of an epidemic, without measuring the entire network of individuals? Christakis and Fowler (2010, cited in Wilson, 2010) found a unique group of users that could predict an epidemic days before its peak in the relevant population. This study continues their work. Instead of exploring millions of online social activities, we suggest investigating the active users (as we define them) in their community and using their activity logs to build a partial network. This network of intensive users can depict the dynamics of a huge social network, in our case Yahoo! Answers intensive activities. Barabási, et al. (2002) explored the connection between topology and network size on real–life networks. Twelve years later, our online Q&A social network study reached the same findings and conclusion: the partial network has several basic topological parameters that correlate with activity parameters of the entire social network and, hence, make it suitable for depicting the dynamic parameters of the huge network. Since exploring online social lives is so interesting and time consuming, we believe that our findings can help the investigation of huge social networks. We call for further investigation of these findings and their implications.
