Sallamari Sallmen (Aalto)
Friday 2018-01-26 14.15 – 15.00
Seminar room T5, T-building
Graphlets in Multilayer Networks
Networks, consisting of nodes and edges, are used to model and analyze a wide array of phenomena in various disciplines. Recently, increasing attention has been given to more realistic network representations such as multilayer networks, which can include different types of interactions between nodes or other additional structural information. However, generalizing concepts devised for ordinary graphs to multilayer networks is in its early stages. In this thesis, the concept of graphlets and algorithms utilizing them to analyze networks are generalized to multilayer networks. Graphlet analysis has been applied to alignment-free network comparison, and here such a comparison method is developed for multiplex networks, which are a type of multilayer networks. The ability of multilayer-graphlet-based distance measures to cluster similar networks is assessed by applying the measures for both real world and random networks, and the performance of these new multilayer methods are compared to methods based on ordinary networks. The results indicate that one can benefit from the multiplex measures when there exists clear multiplex structure in the network.