Babak Ravandi, Gordana Ispirova, Michael Sebek, Peter Mehler, Albert-László Barabási & Giulia Menichetti
Nature Food (2025)

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment. The extensive data gathered on ingredient lists and nutrition facts enables a large-scale analysis of ingredient patterns and degrees of processing, categorized by store, food category and price range. Furthermore, it allows the quantification of the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB makes this information accessible, guiding consumers toward less processed food choices.

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Roberto Murcio, Balamurugan Soundararaj

The accurate estimation of human activity in cities is one of the first steps towards understanding the structure of the urban environment. Human activities are highly granular and dynamic in spatial and temporal dimensions. Estimating confidence is crucial for decision-making in numerous applications such as urban management, retail, transport planning and emergency management. Detecting general trends in the flow of people between spatial locations is neither obvious nor easy due to the high cost of capturing these movements without compromising the privacy of those involved. This research intends to address this problem by examining the movement of people in a SmartStreetSensors network at a fine spatial and temporal resolution using a Transfer Entropy approach.

Read the full article at: arxiv.org

Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino & Hiroki Sayama
npj Complexity

Human collective tasks in teams and organizations increasingly require participation of members with diverse backgrounds working in networked social environments. However, little is known about how network structure and the functional diversity of member backgrounds would interact with each other and affect collective processes. Here we conducted three sets of human-subject experiments which involved 617 university students who collaborated anonymously in a collective ideation task on a custom-made online social network platform. We found that spatially clustered collectives with assortative background distribution tended to explore more diverse ideas than in other conditions, whereas collectives with random background distribution consistently generated ideas with the highest utility. We also found that higher network connectivity may improve individuals’ overall experience but may not improve the collective performance regarding idea generation, idea diversity, and final idea quality.

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Nanotube bridge networks grow between the most abundant photosynthetic bacteria in the oceans, suggesting that the world is far more interconnected than anyone realized.

Read the full article at: www.quantamagazine.org

Francesco Bullo; Pietro Hiram Guzzi

Recent years have seen a remarkable rise in the number of applications of network science in
the fields of biology and medicine. Networks in biology and medicine aim to represent the
organisation of living systems as a set of interacting elements at different scales, from the
subcellular to the population level. The initial application of network science in medicine
primarily focused on understanding the structure of protein interaction networks and using
these relationships to hypothesize new disease genes or novel therapeutic targets. At the same
time, network science has been widely applied in the context of molecular biology, for example
to model biological processes as gene regulatory networks. Now, with the continual influx
of biomedical big data, which is providing increasingly detailed information about various
aspects of molecular biology and medicine, the scale and scope of the network models used in
biology and medicine have skyrocketed. For example, improvements in medical imaging has
greatly facilitated the study of brain interaction networks.
Moving forward, it is imperative to develop approaches that holistically model the
complexity inherent in biological systems. Network science, in particular, has the potential to
answer critical questions in medicine that cannot be addressed through standard approaches.
By capitalizing on tools designed to quantify the fundamental properties of large-scale complex
systems, network science offers a complementary view to that of systems biology, which
tends to focus on basic mechanisms and small to medium-scale biological models. We believe
there has never been a better opportunity to employ network science to make sense of large,
complex biological systems and tackle some of medicine’s most challenging open questions. In this white paper, we review several key areas where network science has the best opportunity to contribute to medical applications and posit several critical future directions for the field.

Read the full article at: netscisociety.net