The problem of maximizing influence spread has been widely studied in social networks, because of its tremendous number of applications in determining critical points in a social network for information dissemination. All the techniques proposed in the literature are inherently static in nature, which are designed for social networks with a fixed set of links. However, many forms of social interactions are transient in nature, with relatively short periods of interaction. Any influence spread may happen only during the period of interaction, and the probability of spread is a function of the corresponding interaction time. Furthermore, such interactions are quite fluid and evolving, as a result of which the topology of the underlying network may change rapidly, as new interactions form and others terminate. In such cases, it may be desirable to determine the influential nodes based on the dynamic interaction patterns. Alternatively, one may wish to discover the most likely starting points for a given infection pattern. We will propose methods which can be used both for optimization of information spread, as well as the backward tracing of the source of influence spread. We will present experimental results illustrating the effectiveness of our approach on a number of real data sets. Copyright © 2012 by the Society for Industrial and Applied Mathematics.