Connect Socially

Multiscale & Integrative compleX Networks: EXperiments & Theories 2021

ONLINE | October 22nd 2021



Network Science for Multidimensional Data Analysis

The coexistence of multiple types of interactions within social, technological and biological networks has moved the focus of the statistical physics of complex systems towards their description as a set of subsystems organized as layers of connectivity. This approach has unveiled that the multilayer nature of complex systems has strong influence in the emergence of collective states and their critical properties, setting a novel paradigm in the past decade.
However, real-world systems are not only multilayer in their nature: they exhibit, simultaneously, a complex organization across multiple scales in their topology, dynamics and function. Recently, many approaches have been proposed to model higher-order interactions different from multiplexity: from simplicial complexes to memory in network flows, from latent topological geometry to multiresolution methods to unravel the hidden geometry of network-driven processes.
All those methods provide powerful tools to analyze complex systems and to unravel the effects of hierarchies from different points of view. However, empirical networks often exhibit multiscale spatio-temporal organization, multilayer relationships and non-trivial geometry.

Aim of MIX-NEXT II. The aim of this Satellite meeting is to balance the contribution of well established leading experts and rising young researchers to review the recent advances in those research fields, with the aim of triggering and igniting new discussions on theoretical and computational solutions required to build a more comprehensive set of tools integrating different perspectives into one, coherent and self-consistent, framework for modeling and analysis of complex networks.
The list of topics that we aim to cover at the conference is the following:
- Mathematical properties of multiscale, multilayer and (hidden) geometric structures
- Empirical measurements for multiscale, multilayer and higher-order networks
- Applications of such models to biological, social, technological and urban systems

Previous editions

 2014 Berkeley, California
 2015 Zaragoza, Spain
 2016 Amsterdam, The Netherlands
 2017 Cancun, Mexico
 2018 Paris, France
 2019 Singapore
 2020 Rome, Italy
 2020 Online


J. Nathan Kutz
University of Washington
Renaud Lambiotte
University of Oxford
Vera Pancaldi
Université Toulouse
Giulia Pullano
Sorbonne Université

Keynote speakers

Raissa d'Souza,
University of California
Vito Latora
Queen Mary University of London


Contributions are welcome as abstracts about published or unpublished research (one page abstract, that may include figures, tables or references).


July 5 Early Registration Deadline
October 22-23, 2021 yrCSS Warm-up
October 25-29, 2021. Conference
October 22, 2021 Satellite event


All the participants have to register to the main conference or to the satellite day (single day)


Time (CET)    Speaker
Oct 22: 14:00    Manlio De Domenico
Opening and Chair

14:10    Vito Latora, Queen Mary University of London
[keynote talk] Dynamical processes in systems with higher-order intteractions

Complex networks have been successfully used to describe the spread of diseases in populations of interacting individuals. Conversely, pairwise interactions are often not enough to characterise social contagion processes, such as the formation of opinions or the adoption of novelties in social systems, all cases where more complex mechanisms of influence and reinforcement are at work. I will first present a higher-order model of social contagion in which a social system is represented by a simplicial complex and contagion can occur through interactions in groups of different sizes [1]. The model shows the emergence of a discontinuous phase transition, a novel phenomenon induced by the presence of higher-order interactions, and of a bistable region where healthy and endemic states co-exist. This result can have practical implications, because it can help explaining why critical masses are required to initiate social changes. I will then discuss other examples of significant effects of higher-order interactions in social processes, showing how interactions in groups of different sizes affects the evolution of cooperation [2] or the stability of synchronised states in simplicial complexes of coupled dynamical systems [3].
14:55    Giulia Pullano, Sorbonne Université
Multi-source data and epidemic modeling to fight COVID-19 epidemic

On March 17, 2020, French authorities implemented a nationwide lockdown to respond to COVID-19 epidemic emergency. Analyzing multiscale mobility network, reconstructed from mobile phone data, we measured how lockdown altered mobility patterns at both local and country scales. Lockdown caused a 65\% reduction in countrywide number of displacements. Mobility drops were unevenly distributed across regions and they were strongly associated with socio-economic, demographic factors and risk aversion. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting, despite the persistence of some long-range trips. Our findings indicate that lockdown was very effective in reducing population mobility across scales and help to predicting how and where restrictions will be the most effective. As countries in Europe relaxed lockdown restrictions after the first wave, test–trace–isolate strategies became critical to maintain the incidence of COVID-19 at low levels. By integrating mobilie phone, virological and surveillance data, we then developed transmission epidemic models, calibrated to French COVID-19 epidemic, to evaluate the performance of the testing system in exit of lockdown. 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system from 11 May to 28 June 2020. While detection rate increased over time, this achievement was likely due to a decreasing epidemic activity. The increase in viral circulation in late summer instead strained the testing system, and led to the 2nd wave. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. As we are still facing COVID-19 pandemic, and there may be other pandemics, epidemiological and behavioural data should be thus collected and open-sources, as they are crucially important to outbreak response. 
15:20    Sarika Jalan, Indian Institute of Technology Indore
Inter-layer pinning: Co-existence of chimera and explosive synchronization in multilayer networks

Random pinning mechanism account for a disorder caused by various physical reasons. All the works so far in this direction are limited to self-feedback pinning which stems out of a single-layer network framework. This Letter, for the first time, investigates interlayer pinning in multilayer networks where the feedback involves the pairs of interconnected nodes. We show that such inter-pinning brings two most intriguing behaviours manifested by coupled nonlinear systems, namely, explosive synchronization transition and chimera state. The emergence of explosive synchronization and layer chimera are substantiated with rigorous mean-field calculations. The random pinning in the interlayer interactions concerns the practical problems where the impact of dynamics of one network on that of other interconnected networks remains elusive, as is the case for many real-world systems.
15:45    Renaud Lambiotte, University of Oxford
Variance and Centrality on Complex Networks

We develop a theory to measure the variance and covariance of probability distributions defined on the nodes of a graph, which takes into account the distance between nodes. Our approach generalizes the usual (co)variance to the setting of weighted graphs and retains many of its intuitive and desired properties. Interestingly, we find that a number of famous concepts in graph theory and network science can be reinterpreted in this setting, including modularity and closeness centrality. As a particular application, we define the maximum variance problem on graphs with respect to the effective resistance distance and we show how the maximum variance distribution is concentrated on the boundary of the graph, and exploit this observation to define a recursive process to uncover the layered structure of a network.
16:10    Break

16:30    Vera Pancaldi, Université Toulouse
Network science of cellular tissues in immuno-oncology

Cancer cells in tumours are surrounded by a rich ecology of healthy and immune cells, known as the tumour microenvironment (TME). The complex interrelations of cells in the TME remain hidden in population bulk datasets. Computational cell type deconvolution approaches can be used to list the types and amounts of cells present in each tumour sample, but these approaches remain approximate and new single-cell methods are bound to make population-based approaches obsolete. Single-cell techniques can identify even the rarest cell types, but they remain extremely complex, expensive – hence impractical to date in clinical settings – and generally fail to capture important TME spatial characteristics. A powerful complementary approach to these sequencing-based approaches is given by imaging. Using specific antibodies in immunofluorescence multiplex imaging, proteins are detected in single cells in tissues, defining cell identity and phenotype in a spatial context. Moreover, new approaches provide spatially resolved single-cell level high-dimensional phenotypes, both at transcriptomics and proteomics level. Networks are a powerful tool to describe the complex spatial patterns in tumour biopsies revealed by these techniques and we have developed algorithms to reconstruct cellular networks efficiently and accurately from biological images and provide metrics that allow us to compare different patient samples and summarise major spatial patterns statistics. The static description of cells in tumours is only the first step towards mechanistic modelling of the complex dynamics interactions between cells of different types (healthy, immune and cancer) but collecting statistical properties from static pictures can be used to constrain computational simulations that take into account internal molecular processes and inter cellular interactions to capture the emergence of a tumour promoting or repressive TME.
16:55    Raissa d'Souza, University of California
[keynote talk] Coevolution, ranking and optimal interventions in multiplex networks

A collection of interdependent networks are at the core of modern society, from electric power grids, to the internet, to social and biological networks. Although there are many different forms of interdependence, one important paradigm is that of multiplex networks, where the same set of nodes can simultaneously have many different types of interactions. For instance, in a social network there can be both affiliative and agonistic interactions between the same individuals. Here we will focus on how to quantify the coevolution among the different types of interactions in multiplex networks, how to successfully develop ranking algorithms when the different types of interactions in a multiplex network are of radically different types, and methods for control interventions that consider trade-offs between the different types of interactions and also consider when the interaction types operate on different timescales. The work presented is inspired by and applied to critical infrastructure systems and macaque monkey societies. Time permitting, recent work on dynamics on hypergraphs will be discussed.
17:40    J. Nathan Kutz, University of Washington
Nonlinear control of networked dynamical systems

We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The {\em sparse identification of nonlinear dynamics} (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on several canonical examples.
18:05    Open discussion

18:25    Closing



Manlio De Domenico
Fondazione Bruno Kessler


Oriol Artime
Fondazione Bruno Kessler


Barbara Benigni
Fondazione Bruno Kessler

Valeria d'Andrea
Fondazione Bruno Kessler

Sebastian Raimondo
Fondazione Bruno Kessler



Alex Arenas Univ. Rovira i Virgili (Spain)
Baruch Barzel Bar-Ilan Univ. (Israel)
Ginestra Bianconi QMUL (UK)
Dirk Brockmann Humboldt Univ. (Germany)
Albert Diaz-Guilera Univ. Barcelona (Spain)
Sergio Gómez Univ. Rovira i Virgili (Spain)