Zhang Zhang, Arsham Ghavasieh, Jiang Zhang & Manlio De Domenico 
Nature Communications volume 16, Article number: 1605 (2025)

Information dynamics plays a crucial role in complex systems, from cells to societies. Recent advances in statistical physics have made it possible to capture key network properties, such as flow diversity and signal speed, using entropy and free energy. However, large system sizes pose computational challenges. We use graph neural networks to identify suitable groups of components for coarse-graining a network and achieve a low computational complexity, suitable for practical application. Our approach preserves information flow even under significant compression, as shown through theoretical analysis and experiments on synthetic and empirical networks. We find that the model merges nodes with similar structural properties, suggesting they perform redundant roles in information transmission. This method enables low-complexity compression for extremely large networks, offering a multiscale perspective that preserves information flow in biological, social, and technological networks better than existing methods mostly focused on network structure.

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Yu Tian, Sadamori Kojaku, Hiroki Sayama, Renaud Lambiotte

Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often have multiple interconnected opinions that can affect different opinions of other individuals, which can be better characterized by matrices. We propose a novel, general framework for modeling such multidimensional interacting dynamics: matrix-weighted networks (MWNs). We present the mathematical foundations of MWNs and examine consensus dynamics and random walks within this context. Our results reveal that the coherence of MWNs gives rise to non-trivial steady states that generalize the notions of communities and structural balance in traditional networks.

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Fengli Xu, Qi Wang, Esteban Moro, Lin Chen, Arianna Salazar Miranda, Marta C. González, Michele Tizzoni, Chaoming Song, Carlo Ratti, Luis Bettencourt, Yong Li & James Evans
Nature Human Behaviour (2025)

The lived experience of urban life is shaped by personal mobility through dynamic relationships and resources, marked not only by access and opportunity, but also inequality and segregation. The recent availability of fine-grained mobility data and context attributes ranging from venue type to demographic mixture offer researchers a deeper understanding of experienced inequalities at scale, and pose many new questions. Here we review emerging uses of urban mobility behaviour data, and propose an analytic framework to represent mobility patterns as a temporal bipartite network between people and places. As this network reconfigures over time, analysts can track experienced inequality along three critical dimensions: social mixing with others from specific demographic backgrounds, access to different types of facilities, and spontaneous adaptation to unexpected events, such as epidemics, conflicts or disasters. This framework traces the dynamic, lived experiences of urban inequality and complements prior work on static inequalities experience at home and work. Xu et al. review applications of urban mobility behaviour data and propose a temporal bipartite network that reveals mobility patterns between people and places. It helps to track urban inequalities in social mixing, facility access and adaptation.

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Moritz U. G. Kraemer, et al.

Nature volume 638, pages 623–635 (2025)

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

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Elma Dervić, Katharina Ledebur, Stefan Thurner & Peter Klimek
Scientific Data volume 12, Article number: 215 (2025)

Comorbidity networks have become a valuable tool to support data-driven biomedical research. Yet, studies often are severely hindered by the availability of the necessary comprehensive data, often due to the sensitivity of health care information. This study presents a population-wide comorbidity network dataset derived from 45 million hospital stays of 8.9 million patients over 17 years in Austria. We present co-occurrence networks of hospital diagnoses, stratified by age, sex, and observation period in a total of 96 different subgroups. For each of these groups we report a range of association measures (e.g., count data, and odds ratios) for all pairs of diagnoses. The dataset provides the possibility to researchers to create their own, tailor-made comorbidity networks from real patient data that can be used as a starting point in quantitative and machine learning methods. This data platform is intended to lead to deeper insights into a wide range of epidemiological, public health, and biomedical research questions.

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