Research in modeling, analyzing and mining large-scale networks has attracted an increasing effort  in the last few years. A major trend of work in network modeling and mining concerns analyzing homogeneous static networks (i.e. one snapshot of a network). However, in real world settings, networks are often dynamic, heterogeneous, and both nodes and links can be described by a set of attributes. The concept of multiplex network has been recently proposed to ease modeling real-world networks  A multiplex network is often represented as a multi-layer network composed of  a set of nodes related to each other with different types of relations.

Multiplex representation is much richer than simple complex networks often used to model complex interaction systems. However, this poses the challenge to provide adequate answers to all basic network analysis tasks that have been studied and provided in the recent few years for the case of homogeneous networks. This include for instance: the problem of node ranking (computing nodes centralities), community detection  link prediction, information diffusion models and network visualization. Almost all work in the field of multiplex network analysis are based on transforming the problem, in a way or another to the classical case of homogeneous network analysis. Existing approaches for multiplex networks include: layer aggregation based approaches or applying ensemble methods on results obtained on each layer aside. Little work has focused on analyzing all layers at once.  First propositions have also been done to adapt community detection algorithms for attributed networks but there is  a need for an in-depth analysis.

The goal of this tutorial is provide an in-depth study of the current state of the art on multiplex network analysis and mining.

Rushed Kanawati, LIPN CNRS UMR 7030