Hao Wang

Human mobility, a pivotal aspect of urban dynamics, displays a profound and multifaceted relationship with urban sustainability. Despite considerable efforts analyzing mobility patterns over decades, the ranking dynamics of urban mobility has received limited attention. This study aims to contribute to the field by investigating changes in rank and size of hourly inflows to various locations across 60 Chinese cities throughout the day. We find that the rank-size distribution of hourly inflows over the course of the day is stable across cities. To uncover the microdynamics beneath the stable aggregate distribution amidst shifting location inflows, we analyzed consecutive-hour inflow size and ranking variations. Our findings reveal a dichotomy: locations with higher daily average inflow display a clear monotonic trend, with more pronounced increases or decreases in consecutive-hour inflow. In contrast, ranking variations exhibit a non-monotonic pattern, distinguished by the stability of not only the top and bottom rankings but also those in moderately-inflowed locations. Finally, we compare ranking dynamics across cities using a ranking metric, the rank turnover. The results advance our understanding of urban mobility dynamics, providing a basis for applications in urban planning and traffic engineering.

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Roland den Boef & René van Woudenberg

Foundations of Science Volume 30, pages 55–72, (2025)

The humanistic disciplines aim to offer explanations of a wide variety of phenomena. Philosophical theories of explanation have focused mostly on explanations in the natural sciences; a much discussed theory of explanation is the causal theory of explanation. Recently it has come to be recognized that the sciences sometimes offer respectable explanations that are non-causal. This paper broadens the discussion by discussing explanations that are offered in the fields of history, linguistics, literary theory, and archaeology that do not seem to fit the causal theory of explanation. We conducted an exploratory survey in acclaimed humanities textbooks to find explicitly so-called explanations and analyze their nature. The survey suggests that non-causal explanations are an integral part of the humanities and that they are of distinct kinds. This paper describes three kinds that are suggested by our survey: teleological, formal, and normative explanations. We suggest that such humanistic explanations strengthen the case for explanatory pluralism.

Read the full article at: link.springer.com

ELENA CANDELLONE, ERIK-JAN VAN KESTEREN, SOFIA CHELMI, and JAVIER GARCIA-BERNARDO

Advances in Complex SystemsVol. 28, No. 03, 2540002 (2025)

Decision-making processes often involve voting. Human interactions with exogenous entities such as legislations or products can be effectively modeled as two-mode (bipartite) signed networks — where people can either vote positively, negatively, or abstain from voting on the entities. Detecting communities in such networks could help us understand underlying properties: for example ideological camps or consumer preferences. While community detection is an established practice separately for bipartite and signed networks, it remains largely unexplored in the case of bipartite signed networks. In this paper, we systematically evaluate the efficacy of community detection methods on projected bipartite signed networks using a synthetic benchmark and real-world datasets. Our findings reveal that when no communities are present in the data, these methods often recover spurious user communities. When communities are present, the algorithms exhibit promising performance, although their performance is highly susceptible to parameter choice. This indicates that researchers using community detection methods in the context of bipartite signed networks should not take the communities found at face value: it is essential to assess the robustness of parameter choices or perform domain-specific external validation.

Read the full article at: www.worldscientific.com

Sina Sajjadi, Pourya Toranj Simin, Mehrzad Shadmangohar, Basak Taraktas, Ulya Bayram, Maria V. Ruiz-Blondet & Fariba Karimi
Scientific Reports volume 15, Article number: 9082 (2025)

During the COVID-19 pandemic, the world witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities in society contribute to health disparities, there has been little attempt to offer a computational and theoretical explanation to establish its plausibility and quantitative impact. Here, we focus on two aspects of structural inequalities: wealth inequality and social segregation. Our computational model demonstrates that (a) due to the inequality in self-quarantine ability, the infection gap widens between the low-income and high-income groups, and the overall infected cases increase, (b) social segregation between different socioeconomic status (SES) groups intensifies the disease spreading rates, and (c) the second wave of infection can emerge due to a false sense of safety among the medium and high SES groups. By performing two data-driven analyses, one on the empirical network and economic data of 404 metropolitan areas of the United States and one on the daily Covid-19 data of the City of Chicago, we verify that higher segregation leads to an increase in the overall infection cases and higher infection inequality across different ethnic/socioeconomic groups. These findings together demonstrate that reducing structural inequalities not only helps decrease health disparities but also reduces the spread of infectious diseases overall.

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Alex Gomez-Marin & Anil K. Seth 
Nature Neuroscience (2025)

The scientific study of consciousness was sanctioned as an orthodox field of study only three decades ago. Since then, a variety of prominent theories have flourished, including integrated information theory, which has been recently accused of being pseudoscience by more than 100 academics. Here we critically assess this charge and offer thoughts to elevate the clash into positive lessons for our field.

Read the full article at: www.nature.com