"The Multiplex Nature of Global Financial Contagions"

Our new article came out today. Here are some details.

Title: "The multiplex nature of global financial contagions"

Authors: R. Maria del Rio-Chanona, Yevgeniya Korniyenko, Manasa Patnam, and Mason A. Porter

Abstract: As illustrated by the 2008 global financial crisis, the financial distress of one country can trigger financial distress in other countries. We examine the problem of identifying such “systemically important” countries (i.e., countries whose financial distress can trigger further distress), which is important for assessing global financial stability. Using data on bilateral financial positions that are split by asset type, we build a multiplex global financial network in which nodes represent countries, edges encode cross-country financial assets of various types, and layers represent asset types. We examine the temporal evolution of a measure of node importance known as MultiRank centrality, and we find that several major European countries decrease in rank and that several major Asian countries increase in rank since 2008. We then develop a multiplex threshold model of financial contagions in which a shock can propagate either within a layer or between layers. We find that the number of systemically important countries can be twice as large when we take into account the heterogeneity of financial exposures (i.e., when using a multiplex network) than in a contagion on an associated aggregate global financial network (i.e., on a monolayer network), as is often examined in other studies. We also study the extent to which buffers can reduce the propagation of financial distress. Our analysis suggests that accounting for both intralayer and interlayer propagation of contagions in a multiplex structure of financial assets is important for understanding interconnected financial systems of countries.

"Layer Communities in Multiplex Networks"

The final coordinates of one of my papers finally appeared with its journal coordinates, although a published version was already available in August 2017. It just took a while for the full special issue in which it appeared to be published, so only now to do we have our volume, issue, and page numbers. This is my first publication in Journal of Statistical Physics.

Here are some other details:

Title: Layer Communities in Multiplex Networks

Authors: Ta-Chu Kao and Mason A. Porter

Abstract: Multiplex networks are a type of multilayer network in which entities are connected to each other via multiple types of connections. We propose a method, based on computing pairwise similarities between layers and then doing community detection, for grouping structurally similar layers in multiplex networks. We illustrate our approach using both synthetic and empirical networks, and we are able to find meaningful groups of layers in both cases. For example, we find that airlines that are based in similar geographic locations tend to be grouped together in a multiplex airline network and that related research areas in physics tend to be grouped together in a multiplex collaboration network.

"Frequency-Based Brain Networks: From a Multiplex Framework to a Full Multilayer Description"

One of my papers just came out in final form. Here are some details.

Title: Frequency-Based Brain Networks: From a Multiplex Framework to a Full Multilayer Description

Authors: Javier M. Buldú and Mason A. Porter

Abstract: We explore how to study dynamical interactions between brain regions by using functional multilayer networks whose layers represent different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as (i) a multilayer network, in which all brain regions can interact with each other at different frequency bands; and as (ii) a multiplex network, in which interactions between different frequency bands are allowed only within each brain region and not between them. We study the second-smallest eigenvalue λ2 of the combinatorial supra-Laplacian matrix of both the multiplex and multilayer networks, as λ2 has been used previously as an indicator of network synchronizability and as a biomarker for several brain diseases. We show that the heterogeneity of interlayer edge weights and, especially, the fraction of missing edges crucially modify the value of λ2, and we illustrate our results with both synthetic network models and real data obtained from resting-state magnetoencephalography. Our work highlights the differences between using a multiplex approach and a full multilayer approach when studying frequency-based multilayer brain networks.

Bonus: This paper has an easter egg. Can you find it? (Hint: This is Spinal Tap.)

The Multiplex Social–Slayage Network of Buffy the Vampire Slayer

Here is the multiplex social–slayage network of Buffy the Vampire Slayer.

The Buffy social network: slayage and love links. https://t.co/xwQ0DoKpAG

— Peter Sheridan Dodds (@peterdodds) March 11, 2018

This figure is definite fodder for talks. Also, if somebody sets up the adjacencies, we should compute some centrality and versatility measures.

A Multiplex Network of Relations Between Probability Distributions

OK, quickly: Which distribution is the most central? :)

(There are quite a few comments on the tweet. I haven't looked at them, but I wonder if people are picking apart inaccuracies? I haven't spent the time to vet this diagram, but I really like the idea!)

how probability distributions are related pic.twitter.com/1i8VHEHdy5

— hardmaru (@hardmaru) August 2, 2017

"Structure of Triadic Relations in Multiplex Networks"

One of my papers, which my collaborators and I first posted on the arXiv and submitted to a journal two years ago, has finally been published in final form. Here are the details.

Title: Structure of Triadic Relations in Multiplex Networks

Authors: Emanuele Cozzo, Mikko Kivelä, Manlio De Domenico, Albert Solé-Ribalta, Alex Arenas, Sergio Gómez, Mason A Porter, and Yamir Moreno

Abstract: Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different 'layers' that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.