Measuring importance of seeding for structural de-anonymization attacks in social networks

Social networks allow their users to make their profiles and relationships private. However, in recent years several powerful de-anonymization attacks have been proposed that are able to map corresponding nodes within two seemingly unrelated datasets solely by considering structural information (e.g., crawls of public social networks and datasets published after sanitization). These algorithms consist of two parts: initial selection of seed nodes and then a propagation phase. In related papers, several seeding procedures are proposed, although detailed comparison is often left unexplored, i.e., how one method differs from the others with respect to the overall outcome of the algorithm. In this paper, beside discussing the existing analysis of seeding methods, we experimentally analyze how different seed selection algorithms perform compared to each other, and we highlight significant differences emerging even in the same or in structurally divergent networks.