MIX-NEXT VI
Network Science for Multidimensional Data Analysis
A fundamental challenge in network analysis lies in the inherent complexity of their structure, dynamics, and function. Real-world networks often display rich, multiscale behaviors, where interactions and patterns emerge across various levels of granularity and timescales. Understanding how different scales, whether spatial, temporal, or informational, interact and contribute to the overall behavior of a system requires methods that extend beyond traditional, single-scale approaches, enabling the integration of information from multiple levels.
Over the last decade, many approaches have been proposed to model higher-order interactions and multiplexity. These include techniques such as simplicial complexes, memory in network flows, latent topological geometry, multiresolution analyses, or methods to uncover the hidden geometry of network-driven processes.
Despite this progress, frameworks designed to capture and understand the complex relationships within these systems across different scales are still incipient and need to be more effectively integrated, so they can offer insights into the design, optimization, and management of complex networks in real-world applications.
Aim of MIX-NEXT.
The goal of this Satellite meeting is to bring together both established experts and emerging young researchers to review recent advancements in the field of multiscale network analysis. We aim to spark discussions on the theoretical and computational solutions needed to develop a more comprehensive toolkit: one that integrates different perspectives into a unified, self-consistent framework for modeling and analyzing complex networks.
Specifically, we will place special emphasis on the following topics:
- Structural and functional robustness, resilience, and the dynamics of multiscale, multilayer, and (hidden) geometric network structures
- Information-theoretic approaches to characterize interconnected systems.
- Practical applications of these network models and metrics across biological, social, technological, and urban systems.
SPEAKERS
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Javier Garcia-Bernardo Utrecht University, The Netherlands |
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Thilo Gross HIFMB & Universität Oldenburg & AWI, Germany |
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Sonia Kéfi Institut des Sciences de l’Évolution de Montpellier, France |
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Johannes Nauta University of Padua, Italy |
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Jasper Van Der Kolk Central European University, Vienna, Austria |
IMPORTANT DATES
| February 25, 2025 | Early Registration | |
| June 2-3, 2025 | Satellites | |
| June 4-6, 2025 | Conference | |
| 8:30, June 3, 2025 | MIXNEXT Satellite |
REGISTRATION
The participants have to register to the main conference or to the satellites only
PROGRAM
Satellite Location: FSE C1.005 at PHS1
| Time (CEST) | Speaker | |
| 8:30 |
Manlio De Domenico and Tomas Scagliarini Opening |
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| 8:40 |
Thilo Gross, HIFMB & Universität Oldenburg & AWI, Germany Biodiversity: The unknown variable Climate Change and Global Biodiversity Loss are often described as two great twin crises of our time. However they are not identical twins. Today the climate system is incredibly well understood while we don't our understanding of biodiversity is still very limited. In particular we do not know why life on earth is so diverse, how diverse it is, or how we should properly measure the diversity. In comparison to community ecology, which studies food webs and plant-pollinator systems, the field of biodiversity has interacted relatively little with network science. Yet, plenty of potential for interaction exists. In this talk I will provide a broad overview of biodiversity research and point out opportunities for network science. Then I will show a particular example of how we use network methods to measure the functional diversity of marine plankton communities. The talk highlights biodiversity as a field where much (and much-needed) progress can still be made by network methods. |
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| 9:15 |
Sonia Kéfi, Institut des Sciences de l’Évolution de Montpellier, France Challenges and opportunities of multi-interaction ecological networks In natural communities, species interact with each other in different ways, including predation, competition, and facilitation. This complex spectrum of interactions constitutes a network of links that mediates ecological communities’ response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species, as well as their consequences for community dynamics. Improving our understanding of the dynamics and resilience of complex ecological systems may rely on how the joint effects of different interactions types explain variations not explained by feeding interactions alone. In this talk, I’ll present recent efforts in analyzing and understanding ecological networks including different types of species interactions. |
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| 9:50 |
Johannes Nauta, University of Padua, Italy Networks in microbial ecology: observing the macro, and guessing the micro Ecological systems are being perturbed on a global scale. Yet, these systems display remarkable feats of resilience or stability and life appears as stubborn as she ever was. Underlying this stubbornness are a plethora of mechanisms, each influencing the others on multiple scales, from the micro to the macro, and back again. In particular, microbial systems, one of the most numerous on the planet, often find themselves at the bottom of all this, but their influence can be felt in essentially all living systems. Additionally, the rise in high-throughput sequencing techniques has generated a vast dataset on these systems, which has given rise to the relatively novel field of macroecology. Herein, macroscopic quantities, such as average species abundances, are observed in natural contexts and seemingly universal patterns emerge. Yet, when observing the macro, what can we say about the micro? What can we say about the networks that underlie the macroscopic patterns? In this talk, I will aim to provide a broad overview of the macroecology of microbial systems and, in light of recent results, will try to convey you that making educated guesses about the micro to explain the macro may not be so bad as it sounds. |
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| 10:30 |
Break |
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| 11:00 |
Javier Garcia-Bernardo, Utrecht University, The Netherlands The Impact of Intersecting Social Contexts on Educational Outcomes Decades of sociological research have extensively examined factors impacting socioeconomic outcomes, often emphasizing social contexts such as families, schools, or neighborhoods. However, understanding their relative importance is challenging because individuals are embedded in complex networks of these social contexts, which interact with each other and with individual characteristics to constrain or facilitate opportunities. Here, we study how the social contexts of children at age 12--before tracking into secondary schools--are linked to educational attainment at age 25. We treat individuals as part of a multiplex network of social contexts, and use statistical models of increasing complexity--from linear models to graph neural networks--to investigate how these different social contexts interact and contribute to educational outcomes. |
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| 11:35 |
Jasper Van Der Kolk, Central European University, Vienna, Austria Renormalization of networks with weak geometric coupling The Renormalization Group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their topology is based on the location of nodes in a hidden metric space, is one of the foundational approaches. However, the current methods assume that the geometric coupling is strong, neglecting that many real networks are weakly geometric. In this talk I introduce the extension of geometric renormalization to the weak coupling regime and show that geometric information is essential for preserving self-similarity for a wide range of couplings. These results underline the importance of geometric effects on network topology, even when the coupling to the underlying space is weak. |
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| 12:00 |
Open discussion |
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| 12:30 |
Closing |
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