Critical phenomena in complex networks: from scale-free to random networks

Within the conventional statistical physics framework, we study critical phenomena in configuration network models with hidden variables controlling links between pairs of nodes. We obtain analytical expressions for the average node degree, the expected number of edges in the graph, and the Landau and Helmholtz free energies. We demonstrate that the network’s temperature controls the average node degree in the whole network. We also show that phase transition in an asymptotically sparse network leads to fundamental structural changes in the network topology. Below the critical temperature, the graph is completely disconnected; above the critical temperature, the graph becomes connected, and a giant component appears. Increasing temperature changes the degree distribution from power-degree for lower temperatures to a Poisson-like distribution for high temperatures. Our findings suggest that temperature might be an inalienable property of real networks.

https://link.springer.com/article/10.1140/epjb/s10051-023-00612-0

LAKE COMO SCHOOL OF ADVANCED STUDIES

Spring School "Computational Social Science: Advances, Challenges and Opportunities (1st edition)"

Villa del Grumello, Como, Italy, May 13-17 2024

https://css.lakecomoschool.org/

Does the brain behave like a (complex) network? I. Dynamics
https://www.sciencedirect.com/science/article/pii/S1571064523002105

#job Data Scientist - Computational Social Science Lab

https://wd1.myworkdaysite.com/en-US/recruiting/upenn/careers-at-penn/job/3401-Walnut-Street/Data-Scientist---Computational-Social-Science-Lab_JR00084578

Link Me Baby One More Time: Social Music Discovery on Spotify

https://arxiv.org/abs/2401.08818

#PhD at ITU at the intersection of AI, Network Science and Computational Social Science

https://candidate.hr-manager.net/ApplicationInit.aspx?cid=119&ProjectId=181658&DepartmentId=3439&MediaId=5

Fast degree-preserving rewiring of complex networks

https://arxiv.org/abs/2401.12047

Mark Newman | Leaders and Best: Networks and Ranking in Sports, Markets, and Society

One of the oldest of network problems is the ranking of individuals, teams, or commodities on the basis of pairwise comparisons between them. For example, if you know which football teams beat which others in a particular year, can you say which team is the best overall? This is a harder problem than it sounds because not all pairs of teams play games in a given season, and also because the outcomes of the games can be ambiguous or contradictory. This talk will introduce the techniques used to solve such ranking problems, with examples from games and sports, consumer research and marketing, and social hierarchies in both animal and human communities, then ask how those techniques can be extended to answer a range of new questions about competition and ranking, including the development of new computer algorithms for ranking, questions about the varying patterns of competition in different sports, and what happens when individuals or teams compete in multiple different ways.

https://www.mivideo.it.umich.edu/playlist/dedicated/293581272/1_gz6diy1y/1_ke40xxtk

"Ambitions for theory in the physics of life" (by William Bialek): https://arxiv.org/abs/2401.15538

[note: Lectures at the 2023 Les Houches Summer School, Theoretical Biophysics]

Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic descriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each case, optimization makes detailed, largely parameter-free predictions that connect quantitatively with experiment

Up to 6 fully-funded #PhD positions in Data Science for Oct 2024, Trieste (Italy): 4 years, no restriction on nationality - applications from candidates from under-represented groups especially encouraged!

http://datascience.sissa.it/apply

Multiple #Postdoc positions in Budapest
https://centerforcollectivelearning.org/jobs

The Computational Inequalities Research Group, led by Orsolya Vásárhelyi is looking for two Postdoctoral Research Fellows for full-time positions (40 hours/week) at the Center for Collective Learning (CCL) at Corvinus University at the Corvinus University of Budapest.

Where does the $$$ in Art Come From? The role of philanthropy.

https://news.northeastern.edu/2024/01/18/art-philanthropy-in-the-us

13 #PhD positions in Machine Learning, Statistics, Logic, Language Technology, and Ethics
Integreat, The Norwegian Centre for Knowledge-driven Machine Learning, https://www.jobbnorge.no/en/available-jobs/job/257181/13-phd-positions-in-knowledge-driven-machine-learning

The physics of financial networks

As the total value of the global financial market outgrew the value of the real economy, financial institutions created a global web of interactions that embodies systemic risks. Understanding these networks requires new theoretical approaches and new tools for quantitative analysis. Statistical physics contributed significantly to this challenge by developing new metrics and models for the study of financial network structure, dynamics, and stability and instability. In this Review, we introduce network representations originating from different financial relationships, including direct interactions such as loans, similarities such as co-ownership and higher-order relations such as contracts involving several parties (for example, credit default swaps) or multilayer connections (possibly extending to the real economy). We then review models of financial contagion capturing the diffusion and impact of shocks across each of these systems. We also discuss different notions of ‘equilibrium’ in economics and statistical physics, and how they lead to maximum entropy ensembles of graphs, providing tools for financial network inference and the identification of early-warning signals of system-wide instabilities.

https://www.nature.com/articles/s42254-021-00322-5

سخنرانی‌های عمومی برخط دانشکده فیزیک بهشتی

🦠 علم شبکه و مدل‌سازی پخش بیماری‌ در حضور مداخله‌‌ها (۱۵:۰۰)
📼 قوس داستانی و خم‌های عاطفی در قصه‌ها (۱۶:۰۰)

چهارشنبه ۲ اسفند ۴۰۲ ساعت ۱۵:۰۰
عباس ریزی — دانشگاه آلتو، فنلاند

شرکت برای همه از طریق این پیوند آزاد و رایگان است:

🔗 meet.google.com/whe-obvb-ger

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@sitpor | sitpor.org
instagram.com/sitpor_media
#سیتپـــــور به خاطر روایتگری در علم

DAMUT Colloquium | 28 Feb, 2024, 12:45 to 13:15
Department of Applied Mathematics, University of Twente (UT)

Consequences of Social Network Structure for Epidemic Interventions

Abbas K. Rizi (Aalto University)

Abstract:
The COVID-19 pandemic has highlighted gaps in our understanding of how epidemics spread and the limitations of simple models in real-world scenarios, particularly when it comes to understanding herd immunity. In this presentation, we will focus on how the structure of contact networks affects the spread of disease and the effectiveness of interventions. We will explore the impact of pharmaceutical interventions, such as vaccination, and non-pharmaceutical measures, like contact tracing, on the trajectory of epidemics. We will consider factors such as behavior-based homophily, group structures, spatial characteristics, and the heterogeneities of contact networks. Additionally, we will introduce an advanced theoretical framework for analyzing temporal dynamics in networks, which is crucial for understanding how diseases spread, how information is disseminated, and how public transportation systems are accessed over time. Finally, we will connect the concept of temporal network reachability with percolation theory, a significant concept in studying complex systems.

https://www.utwente.nl/en/eemcs/damut/damutcolloquium/