Graph Matching On Social Networks Without Any Side Information

Graph matching is an important yet difficult task with many applications in bioinformatics, where it uncovers hidden relationships between species, network security, where it can be used for network de-anonymization, and computer vision, where it solves correspondence problems.When no side information is available, any graph matching algorithm must rely only on the structural information of the two input graphs in order to compute a mapping between the two node sets. One of the most scalable approaches for graph matching uses ideas from percolation theory, where already matched pairs "infect" other neighbouring pairs. In the absence of any side information, such algorithms expect from the user to provide an initial set of pairs, the seeds, from which the percolation can start.In this paper, we propose DiNoiSe, a new distributed percolation graph matching algorithm, which employs the structural information of the network, but no initial seeds or any other node or edge labels. The algorithm takes advantage of the structural features of scale-free graphs (such as the ones behind social networks) and scales well for large networks, without using any additional input. We demonstrate the effectiveness of our algorithm via experiments on synthetic and real-world datasets.