In social networks, nodes correspond to entities and edges to links between them. In most of the cases, nodes are also associated with a set of features. Noise, missing values or efforts to preserve privacy in the network may transform the original network G and its feature vectors F . This transformation can be modeled as a randomization method. Here, we address the problem of reconstructing the original network and set of features given their randomized counterparts G ′ and F ′ and knowledge of the randomization model. We identify the cases in which the original network G and feature vectors F can be reconstructed in polynomial time. Finally, we illustrate the efficacy of our methods using both generated and real datasets.
The method proposed in REF aims to address the problem of reconstructing the original network and set of features given their randomized counterparts.
Reconstructing randomized social networks
SDM
Computer Science