Keynote Speakers
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Elisa Bellotti
University of Manchester, UK Multimode and Multilevel Networks in the Study of Science. Some Theoretical Considerations Following the exponential growth of scientific activities after the 2nd World War, science, its institutional organization and the everyday practices involved in producing it became an important field of enquiry for sociologists, historians and philosophers, and more recently for social physicists and computer scientists. Scholars have traditionally aligned along the two main frameworks of sociology of science on one hand, and of sociology of scientific knowledge (SSK), science and technology studies (STS) and Actor Network theory (ANT) on the other hand. These two frameworks have grown increasingly apart, to the point of allegedly ontological and epistemological incommensurability. Despite the differences, both traditions have largely recognised the importance of social networks for structuring scientific communities. Social networks were at the core of classic sociometric studies, but they were also acknowledged by Kuhn (1970) as efficient techniques for mapping scientific communities and used by STS and subsequently ANT researchers as visualisations to represent the structure of scientific specialties. In this talk I review those attempts to discuss the theoretical and methodological importance of social networks for the study of scientific communities, highlight the key communal elements that emerge from these frameworks, and read them within the realm of relational sociology. I argue that despite the ontological and epistemological differences, these two traditions can be bonded by a joint interest in defining the networked nature of scientific communities, in identifying their network structures and organizations, and in accounting for network hierarchies and dynamics. By taking seriously relational theory we can use the powerful tools of social network analysis in its recent multilevel and multimode developments to analyse and explain the co-construction of cultural/cognitive and social factors. Most importantly, by merging the two traditions, I hope to shed a more informed light to the systematic and often elusive inequalities that still affect the production of science. 
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JPhys COMPLEXITY JOURNAL SPEAKER
Alessio Cardillo Universitat Oberta de Catalunya, Spain A New Opportunity for Network Science: Digital Humanities Despite the availability of huge amounts of digital records, there exists a whole bunch of human activities related with arts and humanities whose records are not digitally available, as information is stored in books, correspondence, libraries, and archives. The systematic use of digital resources in the humanities, as well as of quantitative analysis methods, has led to the birth of the so-called "digital humanities" field. Network science constitute a powerful asset to address questions of interest for scholars working in humanities and non. Using case studies related with translation flows, gender inequalities, and geopolitics of international cooperation, in this talk I will discuss the opportunities (and caveats) stemming from the application of network science to address questions arising from these topics. 
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STEERING COMMITTEE-CHOICE SPEAKER
Giulia Cencetti Aix-Marseille Université, Université de Toulon, CNRS, CPT, Marseille, France Temporal Network Generation: from Small to Large Scale in Time and Topology Analysing temporal networks to generate realistic surrogates induces us to reflect about the set of intrinsic relationships that shape an evolving network. These involve temporal and topological causalities and correlations concerning nodes activity and links existence. We observe how the individual nodes dynamics is linked to that of neighbouring nodes but is also influenced by the global topology. Analogously, the local topology at each time is affected both by the interactions of the few previous time steps and by long-term temporal correlations. In this context we develop a framework to generate temporal networks based on the observation of real-world temporal networks given as input. In a first approximation we only consider the small-scale dynamics: an egocentric perspective in which the activity of a node is only impacted by the activity of the neighbours in a short time range. We then progressively add meso and large scale effects, involving modularity, clustering and memory of more remote time steps. This methodology will allow to obtain surrogate temporal networks to replace real data when the latter are not usable or sharable (particularly useful for social data, often subject to privacy issues). Moreover, it provides an important tool for data augmentation, allowing us to generate temporal networks with an arbitrary number of nodes and time span, using as input original patterns extracted from available datasets of limited size. 
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THE INSTITUTE OF DATA SCIENCE AND ARTIFICIAL INTELLIGENCE SPEAKER
Vittoria Colizza Inserm & Sorbonne Universite, France Behavioral Aspects in the Individual and Spatial Transmission of Mpox Starting May 2022, individuals with mpox (formerly known as monkeypox) were reported by several countries worldwide, outside the regions of West and Central Africa where the disease is endemic and sustained by circulation in animal reservoirs. The mpox global outbreak led to a fast increase in new infected individuals through close contact transmission primarily among men-who-have-sex-with-men (MSM). Focusing on the mpox outbreak in France and using multiple data streams – including outbreak case data, detailed survey data from these cases, vaccination data, sexual behavioral data from surveys in the MSM community undertaken before and after the major mpox outbreak– we constructed different networks to integrate the behavioral aspects relevant to individual and spatial transmission. We showed that attendance to MSM commercial venues drove the rapid initial spread, followed by a rapid decline largely explained by a reduction of sexual activity induced by the perceived risk of infection, and before vaccination could reach mitigating effects at the population level. The study findings highlight the need for early awareness campaigns involving the affected community. 
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Robin Dunbar
University of Oxford, UK The Dynamics of Optimally Structured Egocentric Networks The human social world has a very distinct layered structure, determined largely by the frequency with which we contact our friends. The size and dynamics of these networks are remarkably consistent, occurring not only in human networks but also monkey and ape networks, as well as natural human communities. I will show that the these layers are criticalities that optimise information flow through networks. 
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THE COMPUTER SCIENCE DEPARTMENT @ EXETER SPEAKER
Iacopo Iacopini Northeastern University London, UK The Social Dynamics of Group Interactions Complex networks have emerged as the primary framework for modeling the dynamics of interacting systems. However, networks inherently describe pairwise interactions, while real-world systems often involve interactions among groups of three or more units. In this talk, I will explore social systems as a natural testing ground for higher-order network approaches. I will briefly demonstrate how incorporating higher-order mechanisms can lead to the emergence of novel phenomena, presenting recent results on the influence of structural features and seeding strategies on emergent dynamics. Finally, I will delve into the microscopic dynamics of empirical higher-order structures, examining the mechanisms governing their temporal dynamics at both the individual and group levels. This will involve characterizing how individuals navigate groups and how groups form and dissolve. I will conclude by proposing a dynamical hypergraph model that closely reproduces empirical observations. 
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COMPLENET 2023 BEST SPEAKER
Rafael Prieto-Curiel Complexity Science Hub Vienna, Austria Applying Complexity to Achieve Sustainable Urban Mobility Urban mobility is at a crossroads, grappling with the pervasive impact of cars on cities worldwide. The rise in motorization rates brings about a multitude of challenges, from road safety concerns to environmental degradation and sedentary lifestyles. Despite these pressing issues, budget often prioritize car-centric infrastructure, perpetuating a cycle of dependence. Moreover, the lack of reliable data complicates efforts to analyze and compare mobility across cities, hindering the formulation of effective solutions. In this talk, I will delve into how leveraging data and understanding complex systems can help us understand the way towards achieving sustainable mobility in cities. 
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APPLIED NETWORK SCIENCE JOURNAL SPEAKER
Filippo Radicchi Indiana University Bloomington, USA Modeling Resource Consumption and Transit Efficiency in Transport Systems with Percolation and Multiplex Networks In the first part of the talk, I introduce a percolation model aimed at mimicking the consumption, and eventual exhaustion, of resources in transport networks. In the model, edges forming minimum-cost paths connecting demanded origin-destination nodes are removed if the cost associated with these paths is below a certain budget. As pairs of nodes are demanded and edges are removed, the macroscopic connected component of the graph disappears, i.e., the graph undergoes a percolation transition. I characterize such a transition by means of finite-size scaling analysis, showing that different critical properties emerge depending on whether the budget parameter of the percolation model is bounded or not. In the second part of the talk, I consider a multiplex network with a fast layer embedded in a slow one. To move between any pair of nodes, one can then use either the fast or slow layer, or both, with a switching cost when going from one layer to the other. I take advantage of analytical and numerical arguments to show that the optimal structure minimizing the transit time in the network is characterized by symmetry breaking, indicating that it is sometimes better to avoid serving a whole area in order to save on switching costs, at the expense of using more the slow layer. I finally discuss the relevance of the multiplex model to estimate the efficiency of the subway system in the cities of Atlanta, Boston, and Toronto. 
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