Vesa Kuikka & Kimmo K. Kaski 

Scientific Reports volume 14, Article number: 28069 (2024)

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

Read the full article at: www.nature.com

Jin Han, Balaraju Battu, Ivan Romić, Talal Rahwan, Petter Holme

Large language models (LLMs) are increasingly used to model human social behavior, with recent research exploring their ability to simulate social dynamics. Here, we test whether LLMs mirror human behavior in social dilemmas, where individual and collective interests conflict. Humans generally cooperate more than expected in laboratory settings, showing less cooperation in well-mixed populations but more in fixed networks. In contrast, LLMs tend to exhibit greater cooperation in well-mixed settings. This raises a key question: Are LLMs about to emulate human behavior in cooperative dilemmas on networks? In this study, we examine networked interactions where agents repeatedly engage in the Prisoner’s Dilemma within both well-mixed and structured network configurations, aiming to identify parallels in cooperative behavior between LLMs and humans. Our findings indicate critical distinctions: while humans tend to cooperate more within structured networks, LLMs display increased cooperation mainly in well-mixed environments, with limited adjustment to networked contexts. Notably, LLM cooperation also varies across model types, illustrating the complexities of replicating human-like social adaptability in artificial agents. These results highlight a crucial gap: LLMs struggle to emulate the nuanced, adaptive social strategies humans deploy in fixed networks. Unlike human participants, LLMs do not alter their cooperative behavior in response to network structures or evolving social contexts, missing the reciprocity norms that humans adaptively employ. This limitation points to a fundamental need in future LLM design — to integrate a deeper comprehension of social norms, enabling more authentic modeling of human-like cooperation and adaptability in networked environments.

Read the full article at: arxiv.org

ERIC NGUYEN, et al.

SCIENCE 15 Nov 2024 Vol 386, Issue 6723

Evo is a foundation model that is designed to capture two fundamental aspects of biology: the multimodality of the central dogma and the multiscale nature of evolution. The central dogma integrates DNA, RNA, and proteins with a unified code and predictable information flow, whereas evolution unifies the vastly different length scales of biological function represented by molecules, pathways, cells, and organisms. Evo learns both of these representations from the whole-genome sequences of millions of organisms to enable prediction and design tasks from the molecular to genome scale. Further development of large-scale biological sequence models like Evo, combined with advances in DNA synthesis and genome engineering, will accelerate our ability to engineer life.

Read the full article at: www.science.org

Dirk Helbing , Sachit Mahajan , Dino Carpentras , Monica Menendez , Evangelos Pournaras , Stefan Thurner , Trivik Verma , Elsa Arcaute , Michael Batty and Luis M. A. Bettencourt

Phil. Trans. Roy. Soc. A Volume 382 Issue 2285

The digital revolution, fuelled by advancements in social media, Big Data, the Internet of Things and Artificial Intelligence, is reshaping our urban landscapes into ‘participatory cities’. These cities leverage digital technologies to foster citizen engagement, collaborative decision-making and community-driven urban development, thus unlocking new potentials while confronting emerging threats. Such technologies are empowering individuals and organizations in ways that were unimaginable just a few years ago. They do, however, introduce new risks and vulnerabilities that must be carefully managed. Hence, socio-technical innovation is urgently needed. In this connection, open-source technologies, participatory approaches and new forms of governance are becoming more popular and relevant. This theme issue looks into the tangible impacts of these technological advancements, with a focus on participatory cities. It aims to explain how digital tools are used in cities to tackle urban challenges, improve governance and promote sustainability. Through a collection of in-depth analyses, case studies and real-world examples, this issue seeks to offer a comprehensive understanding of the digital governance frameworks underpinning participatory cities. By offering a platform for multidisciplinary discourse, this theme issue endeavours to contribute to the broader narrative of shaping a more resilient, sustainable and democratic urban future in the digital age.

Read the full article at: royalsocietypublishing.org

See Also: Theme issue ‘Co-creating the future: participatory cities and digital governance’