Research has shown the effectiveness of inferring user interests from social neighbors, also called "social profiling". However, the evolution in the social profile is not widely taken into consideration. To overcome this drawback, we propose a time-aware social profiling method that considers the temporal factors of the information and the relationships between the user and his/her social neighbors. This method aims at weighting user interests in the social profile, by applying a time decay function. The temporal score of a given interest is computed by combining the temporal score of information used to extract the interests with the temporal score of individuals who share the information in the network. The experiments conducted on a co-authorship network, DBLP showed that the time-aware social profiling process applying our proposed time-aware method outperforms the existing time-agnostic social profiling process. The combinatorial analysis and the parametric study led us to observe that in the context of co-authorship network, the individual temporal score has more influence than the information temporal score. As this kind of network does not exhibit a rapid evolution of information and relationships, to obtain a relevant social profile, the information should be damped slowly.