Social Networking Sites , such as Twitter and LinkedIn, are clear examples of the impact that the Web 2.0 has on people around the world, because they target an aspect of life that is extremely important to anyone: social relationships. The key to building a social network is the ability of finding people that we know in real life, which, in turn, requires those people to make publicly available some personal information, such as their names, family names, locations and birth dates, just to name a few. However, it is not uncommon that individuals create multiple profiles in several social networks, each containing partially overlapping sets of personal information. As a result, the search for an individual might require numerous queries to match the information that is spread across many profiles, unless an efficient way is provided to automatically integrate those profiles to have an holistic view of the information on the individual. This calls for efficient algorithms for the determination (or reconciliation) of the profiles created by an individual across social networks. In this paper, we build on a previous research of ours and we describe LIAISON (reconciLIAtion of Individuals profiles across SOcial Networks), an algorithm that uses the network topology and the publicly available personal information to iteratively reconcile profiles across n social networks, based on the existence of individuals who disclose the links to their multiple profiles. We evaluate LIAISON on real large datasets and we compare it against existing approaches; the results of the evaluation show that LIAISON achieves a high accuracy.