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Multiscale & Integrative compleX Networks: EXperiments & Theories 2022

ONLINE | July 14 2022

 #MixNext22

MIX-NEXT III

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 III. 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
 2021 Online

INVITED SPEAKERS



Giulia Cencetti
Bruno Kessler Foundation, Italy
Baruch Barzel
Bar-Ilan University, Israel
Sabrina Maniscalco
University of Helsinki, Finland
Martin Rosvall
Umeå University, Sweden

KEYNOTE SPEAKERS



Orit Peleg
University of Colorado Boulder
Mason Porter
University of California Los Angeles

IMPORTANT DATES



July 11 - 24, 2022 Satellite & school
July 25 - 29, 2022 Main Conference
July 14, 2022 Satellite event

REGISTRATION



The participants have to register to the main conference or to the satellite & school only

PROGRAM



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

 
14:15    Sabrina Maniscalco, University of Helsinki, Finland
[invited talk] Spatial search by continuous-time quantum walks on the Internet network

We study spatial search with continuous-time quantum walks on real complex networks. We use smaller replicas of the Internet network obtained with a recent geometric renormalization method introduced by García-Pérez et al. (2017). This allows us to infer for the first time the major properties of a quantum spatial search algorithm on real networks. By simulating numerically the dynamics and optimizing the coupling parameter, we study the optimality of the algorithm and its scaling with the size of the network, showing that on average it is better than the classical scaling, linear in N, but it does not reach the ideal quadratic speedup that can be achieved, e.g. in complete graphs.

 
14:40    Giulia Cencetti, Bruno Kessler Foundation, Italy
Neighbourhood matching creates realistic surrogate temporal networks

Temporal network data sets are essential for modeling and understanding systems whose behavior vary in time, from social interactions to biological systems. Often, however, real data are prohibitively expensive to collect or unshareable due to privacy issues. A promising solution is `surrogate networks', synthetic graphs with the properties of real-world networks. Until now, the generation of realistic surrogate temporal networks has remained an open problem, due to the difficulty of capturing both the temporal and topological properties of the input network, and all their correlations in a scalable model. We propose a novel simple method to explore a temporal network, consisting in decomposing it in its building blocks, namely local temporal neighborhoods of each node with short memory. We then use them to generate a new network from scratch. Basically, the essential information that we use from the original graph to build the new one concerns the behavior of each node in the short time distance, i.e. which connections it creates, eliminates, or maintains, given the connections in the few previous time steps. We thus generate a new pattern of behavior by preserving the short-term temporal correlation of each node. Not only our method can generate real interaction patterns, but it is also able to capture the intrinsic temporal periodicity of the network and to generate temporal graphs with an execution time lower of multiple orders of magnitude with respect to other similar models.
 
15:05    Baruch Barzel, Bar-Ilan University, Israel
Constructing dynamically predictive networks

The state of a complex system is often characterized by the dynamic activities of all its nodes, from the excitation of neurons in brain networks to the expression levels of genes in subcellular interactions. The dynamics around these states are then captured by the system's response to activity perturbations, e.g., a local spike in neuronal activity, an outbreak of an epidemic or a sudden hike in the expression of one or several genes. How then do networks respond to such perturbations? Will they remain stable and witness the perturbation decay, or will they lose stability and transition to an entirely new state? Will their response be rapid or slow? Will it be dispersed throughout the network or condense on specific nodes? Encoded within the system’s stability matrix, the Jacobian, the answer to all these questions is obscured by the scale and diversity of the relevant systems, their multiscale parameter space, and their nonlinear interaction dynamics. To penetrate this complexity, we develop the dynamic Jacobian ensemble, which allows us to systematically investigate the fixed-point dynamics of a broad range of network-based models. We find that real-world Jacobians exhibit universal scaling patterns in which structure and dynamics are deeply intertwined. Once constructed, these unique - and most crucially, unexplored, Jacobians map each combination of topology and dynamics into an effective network, whose link weights adapt to capture the effect of the system's nonlinearity. Hence, identical networks will acquire distinct link weights, depending on the nature of their interaction dynamics - social, biological or technological. The result: effective network maps, whose weighted topology is designed to predict precisely whether the system is stable or unstable, rapid or slow, dispersed or condensed.
 
15:30    Orit Peleg, University of Colorado Boulder
[keynote talk] Physical Computation in Insect Swarms

Our world is full of living creatures that must share information to survive and reproduce. As humans, we easily forget how hard it is to communicate within natural environments. So how do organisms solve this challenge, using only natural resources? Ideas from computer science, physics and mathematics, such as energetic cost, compression, and detectability, define universal criteria that almost all communication systems must meet. We use insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen’s location.
 
16:15    Break


 
16:40    Martin Rosvall, Umeå University, Sweden
Mapping flows on multilayer networks with incomplete observations

Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, flow-based community detection methods can highlight spurious communities in sparse networks with missing links. Current Bayesian approaches developed to overcome this problem do not work for incomplete observations in multilayer networks that describe network flows. To overcome this gap, we extend the idea behind the Bayesian estimate of the map equation to enable more robust community detection in multilayer networks. We derive an empirical Bayes estimate of the transitions rates and show how an efficient implementation in the community-detection method Infomap provides more reliable communities even with a significant fraction of data missing.
 
17:05    Mason Porter, University of California Los Angeles
[keynote talk] Opinion Dynamics on Generalized Networks

I will discuss the modeling of opinion dynamics on generalized networks. I will focus primarily on bounded-confidence models (BCMs), in which nodes have continuous-valued opinions and update those opinions when they interact with nodes with sufficiently similar opinions. I will discuss generalizations of BCMs to hypergraphs and adaptive networks. I will discuss consensus, polarization, and fragmentation of opinions in these models. I will also discuss "opinion jumping" (a new phenomenon) in a BCM on hypergraphs and the role of homophily in a BCM on adaptive networks. Time permitting, I may also briefly discuss other opinion models and the coupling of opinion dynamics and disease spread.
 
17:50    Open discussion


 
18:30    Closing


 

ORGANIZERS



Manlio De Domenico
University of Padua

 

Oriol Artime
Fondazione Bruno Kessler

 

Valeria d'Andrea
Fondazione Bruno Kessler

  

SCIENTIFIC COMMITTEE



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