MIX-NEXT V
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 revealed that the multilayer nature of complex systems strongly influences the emergence of collective states and 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, emerging 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, 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.
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.
In this edition, we aim at giving special emphasis on the following topics:
- Structural/functional robustness and resilience of multiscale, multilayer and (hidden) geometric structures.
- Information-theoretic tools to characterize such interconnected systems.
- Applications of such network models and metrics to biological, social, technological and urban systems.
INVITED SPEAKERS
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Beatriz Arregui-García IFISC (CSIC-UIB), Spain |
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Anne Florence Bibtol Swiss Federal Institute of Technology in Lausanne |
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Marilyn Gatica Northeastern University London, UK |
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Gerardo Iñiguez Tampere University, Finland |
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Henrik Jeldtoft Jensen Imperial College London, UK |
IMPORTANT DATES
| July 15, 2024 | Early Registration | |
| September 2-3, 2024 | Satellites | |
| August 30-31, 2024 | Warm-up | |
| September 2-6, 2024 | Conference | |
| September 2, 2024 | MIXNEXT Satellite |
REGISTRATION
The participants have to register to the main conference or to the satellites only
PROGRAM
| Time (GMT) | Speaker | |
| 9:30 |
Oriol Artime, Valeria d'Andrea and Tomas Scagliarini Opening |
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| 9:35 |
Henrik Jeldtoft Jensen, Imperial College London, UK Evolutionary adaptive dynamics of networks: The Tangled Nature model of evolutionary ecology. The Tangled Nature model is a framework that can be used to analyses the structures emerging when collections of different interdependent types is driven through type-space by reproduction, mutation and adaptation. The model is individual based and applicable to biological evolution at micro to macro scale and is also relevant to e.g. cultural evolution, microbial response to antibiotics [1]. The evolutionary dynamics moves occupancy about on a fixed hard-wired random interaction network consisting of in potentia types. Only a small fraction of the entire hard-wired background network is ever occupied. The evolutionary dynamics makes the population collectively adapt through dynamics that is intermittent at systemic scale. We will discuss how the properties (degree distribution, connectivity, stability, etc.) of the ever-changing network of occupied types evolve as it collectively adapts. Dynamical hierarchies indicated by the micro dynamics occurring at smooth constant rate while the systemic level dynamics of the occupancy is intermittent. The hierarchical nature of the collective evolutionary dynamics can also be addressed by use of information theoretic measures of individuality emerging at different scales [2]. References [1] H.J. Jensen, Tangled Nature: A model of emergent structure and temporal mode among co-evolving agents. European Journal of Physics 40, 014005 (2018). [2] Hardik Rajpal, Clem von Stengeld, Pedro A.M. Medianoa, Fernando E. Rosasa, Pablo A. Marquetj, and Henrik J. Jensen, Information dynamics and the emergence of high-order individuality in ecosystems. |
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| 10:10 |
Beatriz Arregui-García, IFISC (CSIC-UIB), Spain Patterns in Temporal Networks with Higher-Order Egocentric Structures Classical representations of networks often fail to fully capture the entire complexity of real-world systems. While more sophisticated frameworks, such as temporal networks, offer greater flexibility in describing time-varying interactions, they come at the cost of a more complex representation and analysis. Longa et al. (2022) recently proposed an egocentric perspective for temporal networks that allows to represent the salient features of the network in a succinct way. This approach consists in considering the evolution of a node’s neighbourhood (ETN) for a small number of temporal layers. The use of the ego perspective has proven to be a useful tool for characterizing temporal networks relative to social interactions. However, this approach does not consider higher-order interactions which have been proven to affect a variety of dynamical processes in networks. Considering these two aspects, the node egocentric on one side and the higher-order on the other, we propose a characterization of temporal networks via higher-order egocentric structures. We generalize the concept of ETN from graphs to hypergraphs, defining the hyper egocentric temporal neighborhood (HETN). We use this framework to analyze different datasets of proximity contact data, also zooming in on the behavior of individual nodes within the same dataset. We observe that considering second order structures in hyper egocentric temporal neighbourhoods in the analysis of temporal datasets is fundamental for capturing the intrinsic variability in social interactions since it unveils behaviours that could not be appreciated if limiting the analysis to the first order. |
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| 10:35 |
Marilyn Gatica, Northeastern University London, UK Applications of functional high-order interactions: healthy aging and transcranial ultrasound stimulation The brain interdependencies can be studied from either a structural perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity," FC). Notably, while structural connectivity is inherently pairwise (involving white-matter fibers projecting from one region to another), FC is not limited to pairwise interactions. Despite this, many FC analyses predominantly concentrate on pairwise statistics and often neglect higher-order interactions. In this study, we show the extent of high-order interactions in fMRI data, exploring two applications: healthy aging and transcranial ultrasound stimulation. |
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| 11:00 |
Break |
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| 11:30 |
Anne Florence Bibtol, Swiss Federal Institute of Technology in Lausanne Impact of population spatial structure on mutant fixation, from models on graphs to the gut Microbial populations often have complex spatial structures, with homogeneous competition holding only at a local scale. Population structure can strongly impact evolution, in particular by affecting the fixation probability of mutants. I will first discuss a general model for describing structured populations on graphs. I will show that by tuning migration asymmetry in the rare migration regime, the star graph transitions from amplifying to suppressing natural selection. Next, I will discuss the impact of increasing migration rates. In particular, I will show that suppression of selection is pervasive in the regime of frequent migrations. Then I will show that the specific structure of the gut, with hydrodynamics and gradients of food and bacterial concentrations, can increase the fixation probability of neutral mutants. Our results can be rationalized by introducing an active population, which consists of those bacteria that are actively consuming food and dividing. Thus, the specific environment of the gut enhances neutral bacterial diversity. |
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| 12:05 |
Gerardo Iñiguez, Tampere University, Finland Simple models reveal dynamical interplay of mechanisms in social networks Social networked phenomena, like group formation and communication, are hard to quantify and predict. Here we’ll show that simple mathematical models, despite lacking details, are good at uncovering underlying mechanisms and how their dynamical interplay leads to segregation and heterogeneity in social networks. We’ll focus on the universalities these models reveal and what they tell us about human behaviour off- and online. References: G. Iñiguez et al. Universal patterns in egocentric communication networks. Nature Communications 14, 5217 (2023). https://doi.org/10.1038/s41467-023-40888-5 A. Asikainen et al. Cumulative effects of triadic closure and homophily in social networks, Science Advances 6, eaax7310 (2020). https://doi.org/10.1126/sciadv.aax7310 |
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| 12:30 |
Open discussion |
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| 13:00 |
Closing |
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ORGANIZERS
SCIENTIFIC COMMITTEE
| Alex Arenas | Univ. Rovira i Virgili (Spain) | |
| Sergio Gómez | Univ. Rovira i Virgili (Spain) |