Keynote Speakers
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José S. Andrade Jr.
Federal University of Ceará, Fortaleza, Brazil From Correlations to “Interactions”: Applications of Maximum Entropy Models to Finance and Neuroscience In the first part of the talk, we analyze the network of transactions among cities based on the electronic invoice database for the municipalities in the Ceará state, Brazil. It consists of approximately 3.7 billion records, registered during the period between the years 2016 to 2019. We use modularity algorithms to detect the partition which provides the shortest description length and captures the optimal community structure of the network in terms of its associated flow dynamics. From the inter-City network of traded products, we also build bipartite structures considering both selling and buying activities and use the revealed comparative advantage (RCA) concept to define a non-monetary and binary activity index that can distinguish the RCA of a city in a class of goods or services as evidenced by trade flows. Finally, through the pairwise Maximum Entropy Model, we can associate to the largest communities their corresponding binary Ising-like Hamiltonian models. In an analogy with critical phenomena, our results reveal that each community operates at a "temperature" that is close to the corresponding "critical point", suggesting a high degree of "economic cohesiveness" in its trade network of cities. In the second part of the talk, we analyze time-averaged experimental data from in vitro activities of neuronal networks. Using a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our results also indicate that these models display characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is 𝑇0=1, this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior. |
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Celia Anteneodo
Department of Physics PUC, Rio de Janeiro, Brazil Opinion dynamics and epidemics: impact of network structure We will discuss opinion dynamics, with a particular focus on how network structure influences phase transitions between collective states, addressing scenarios where individuals facing binary choices are influenced by a group of the contacts within a network (social pressure), as well as situations where individuals must choose from multiple equally attractive alternatives (which can occur when making crucial as well as everyday decisions). Attention will be paid to theoretical approaches, such as the pair-approximation, which have proven successful in describing and predicting collective states in the former case. In the context of epidemics, we will discuss how attitudes toward mitigation strategies (such as social isolation, mask wearing, and handwashing) are shaped by perceptions of the epidemic, which are influenced by both local and global information, and potentially distorted by misinformation. For the susceptible-infected-recovered (SIR) dynamics on complex networks, we observe that local awareness can increase the epidemic threshold, delay the peak prevalence, and reduce the overall outbreak size. However, these effects are moderated by network heterogeneity. Our findings suggest that the dynamics is not well captured by the heterogeneous mean-field theory, indicating the need for further refinement of theoretical approaches. |
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COMPLENET 2024 BEST SPEAKER
Carmen Cabrera-Arnau University of Liverpool, UK Advancing human mobility research through better use of digital trace data The growing use of digital trace data (DTD) for the study of human mobility, such as location data from smartphone apps, has opened exciting research avenues in complexity science and network analysis. DTD enables analyses at high spatial and temporal granularity, with broad geographic coverage and near real-time availability, offering unprecedented insights into how populations move. However, working with DTD comes with methodological and epistemological challenges, such as missing data and demographic or socioeconomic biases tied to digital access and platform-specific user bases. For example, mobility data derived from social media often over-represents younger, urban populations, raising concerns about representativeness and the generalisability of research findings. Despite these challenges, I argue that DTD remains a valuable resource when appropriately processed. In this keynote, I will discuss the state of the art in bias mitigation and data integration techniques to improve population displacement estimates. I aim to encourage a more nuanced and responsible approach to mobility data, leading to stronger, more ethical applications in network analysis and beyond.
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Philipp Lorenz-Spreen
Max Planck Institute and Center Synergy of Systems and Center for Scalable Data Analytics and Artificial Intelligence TUD Dresden University of Technology, Germany Digital Media and Democracy: The Complex Relationship Between Online Networks and Politics Around the World Digital media has changed the way public spheres function around the world, specifically in the information environment of the internet. Moving from mostly one-to-many communication to a many-to-many system has increased its complexity. In addition, big platforms are curating information flows algorithmically and optimize them for engagement.In this talk I will discuss ways to better understand how those transitions are affecting democracies globally and political behaviour in particular. Phenomena such as affective polarization, the rise of populism, the spread of misinformation and diminishing trust in institutions are developments of concern, but also go high complexity. However, methods from data science, network science but also causal inference and experimentation, are enabling increasingly many approaches to achieve this goal of empirically describing the complex mechanisms at play between human behavior, technology and politics.
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APPLIED NETWORK SCIENCE KEYNOTE SPEAKER
Esteban Moro Northeastern University, Boston, USA Beyond the Census: Understanding Urban Areas Through Behavioral Mobility Data In urban studies, traditional census data provides a static snapshot of cities, often missing the dynamic, real-time interactions that shape urban life and underpin the resilience of our communities. In this talk, I will present our recent research on understanding the dynamics of our cities by analyzing massive behavioral mobility data from mobile phones, credit cards, or social media and its relationship with networked inequalities, such as experienced segregation, access to healthy food, adaptation to the recent pandemic, and public transportation interventions. I will also discuss the methodological challenges and opportunities of using these datasets for population-wide analysis, from managing potential biases to designing better causal inferences of the impact of policies. Finally, I will comment on potential data-driven interventions to reinforce the social fabric in cities and mitigate the detrimental impacts of networked inequalities.
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Francisca Ortiz-Ruiz
Universidad Mayor, Santiago, Chile The Relevance of the Micro Level of Networks: A Relational Approach to Care and Social Support This research seeks to understand the care and support networks of older people in Santiago de Chile and how its understanding could lead to informed interventions at a micro-level. This study aims to identify/describe older people's care (perceived) and support (experienced) networks, emphasising gender and age inequalities and assessing changes in their networks. This presentation will be based on fieldwork in a community centre between May and September 2022 with three waves of interviews, collecting information on the complete network within the community centre and their egocentric networks outside the institution. In this presentation, I will share the first exploration of this data, which is divided into three main aspects. The first is the theoretical and methodological approach to care and support from a network perspective. Secondly, I will explain the care and support networks of older people. Finally, I will focus on the friendship networks of the participants to assess how relevant the changes over time are and whether other aspects influence these interpersonal relationships. 
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