Social networks have become a major focus of research in recent years, initially directed towards static networks but increasingly, towards dynamic ones. In this paper, we investigate how different pre-processing decisions and different network forces such as selection and influence affect the modeling of dynamic networks. We also present empirical justification for some of the modeling assumptions made in dynamic network analysis (e.g., first-order Markovian assumption) and develop metrics to measure the alignment between links and attributes under different strategies of using the historical network data. We also demonstrate the effect of attribute drift, that is, the importance of individual attributes in forming links change over time.