Metric projection for dynamic multiplex networks

Abstract

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time step, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events.

Differential Network Analysis and Graph Classification: A Glocal Approach

“…NetDA based on the HIM distance has been used in metagenomics [52], MEG neuroimaging [21], liver high-throughput oncogenomics [19] and oncoimmunol-ogy [39]. Moreover, the same method has found applicability also out of computational biology, e.g., socioeconomics [32] or even in multiplex network theory [29]. Here we present, after a brief summary of the main definitions, one application example in neurogenomics and one in developmental functional genomics.…”