Homophily Within and Across Groups

If you are looking for a network model that distinguishes between [local] homophily within small groups and [global] homophily across larger, more diverse communities, you shall not miss our new pre-print:

https://arxiv.org/abs/2412.07901

How do similarities shape our connections—and what does that mean for spreading ideas, trends, or diseases?

Traditional models assume a simple rule: people connect with others like them. But our research goes further. We’ve created a model that separates local homophily—strong bonds within close-knit groups—from global homophily, the weaker links across broader communities. This distinction helps explain complex social behaviors and how they impact network dynamics.

Using a maximum entropy approach, our model quantifies these layers of homophily and their influence on networks. One key finding is that different levels of homophily lead to unique percolation behaviors—shifts in how networks stay connected or fragment under certain conditions. We also discovered that these interactions affect critical thresholds for spreading phenomena, from viral outbreaks to information diffusion.

By applying our model to diverse real-world datasets, we demonstrated its ability to capture fine-grained patterns in networks. The insights go beyond theory—they have real implications for designing better public health interventions, optimizing information campaigns, and understanding the role of community structures in amplifying or limiting spread.

So, if you are looking for a network model that distinguishes between [local] homophily within small groups and [global] homophily across larger, more diverse communities, you shall not miss our new pre-print: https://arxiv.org/abs/2412.07901

Open #postdoc position on spatial analysis and epidemic modeling in the framework of an interdisciplinary Horizon project.

https://www.isi.it/news/open-position-postdoc-siesta/

The Department of Demography at the University of California, Berkeley, invites applications for a #Postdoc Scholar position in the research group of Prof. Ayesha Mahmud. This position is part of a new NIH-funded project titled “Novel Data and Approaches for Dynamic Modeling of Human Behavior and Infectious Disease Ecology.” The successful candidate will work on several studies focused on understanding how human behavior and demographic dynamics interact with climate change to influence the spread and evolution of infectious diseases.

https://aprecruit.berkeley.edu/JPF04716

UCLA's Department of Mathematics is advertising for a new faculty member as part of the university's Hispanic-Serving Institution initiative: https://recruit.apo.ucla.edu/JPF10045

A cluster hire in the departments of Chemistry, Mathematics, and Physics and Astronomy

Deadline: 20 January 2025

a 3-year #Postdoc to join us at NYUAD to work on parenting, childhood inequalities, and parenting norms in international and comparative perspective using experimental methods. Deadline to apply: February 14, 2025. To apply: apply.interfolio.com/160976

Network Renormalization

The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling constants, across different levels of resolution. It also provides a way to identify critical points of phase transitions and study the system's behaviour around them by distinguishing between relevant and irrelevant details, the latter being unnecessary to describe the emergent macroscopic properties. In traditional physical applications, the RG largely builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining schemes. More recently, various approaches have tried to extend RG concepts to the ubiquitous realm of complex networks where explicit geometric coordinates do not necessarily exist, nodes and subgraphs can have very different properties, and homogeneous lattice-like symmetries are absent. The strong heterogeneity of real-world networks significantly complicates the definition of consistent renormalization procedures. In this review, we discuss the main attempts, the most important advances, and the remaining open challenges on the road to network renormalization.

https://arxiv.org/abs/2412.12988

Statistical Laws in Complex Systems

Statistical laws describe regular patterns observed in diverse scientific domains, ranging from the magnitude of earthquakes (Gutenberg-Richter law) and metabolic rates in organisms (Kleiber's law), to the frequency distribution of words in texts (Zipf's and Herdan-Heaps' laws), and productivity metrics of cities (urban scaling laws). The origins of these laws, their empirical validity, and the insights they provide into underlying systems have been subjects of scientific inquiry for centuries. This monograph provides an unifying approach to the study of statistical laws, critically evaluating their role in the theoretical understanding of complex systems and the different data-analysis methods used to evaluate them. Through a historical review and a unified analysis, we uncover that the persistent controversies on the validity of statistical laws are predominantly rooted not in novel empirical findings but in the discordance among data-analysis techniques, mechanistic models, and the interpretations of statistical laws. Starting with simple examples and progressing to more advanced time-series and statistical methods, this monograph and its accompanying repository provide comprehensive material for researchers interested in analyzing data, testing and comparing different laws, and interpreting results in both existing and new datasets.

https://arxiv.org/abs/2407.19874

هم‌جوری در شبکه‌های اجتماعی در حضور گروه‌ها

🎞 ویدیو در یوتیوب
🎧 فایل صوتی

در شبکه‌های اجتماعی ارتباطات افراد متاثر از اشتراکات و شباهت‌های بین اوناست. آدم‌ها معمولا با کسانی در ارتباطن که احساس نزدیکی بیشتری باهاشون می‌کنن. از قدیم هم گفتن: کبوتر با کبوتر، باز با باز. به این اصل هم‌جوری یا هوموفیلی میگن. مثلا مشاهده شده که آدم‌هایی که واکسن می‌زنن دوستاشون هم واکسن می‌زنن و آدمایی که دوست ندارن واکسن بزنن معمولا دوستاشون هم از واکسن زدن طفره می‌رن. این‌که هم‌جوری بین افراد در مسئله واکسن زدن چقدر شدید باشه، یعنی چند نفر از دوستان و آشنایان هر نفر مثل اون فکر یا عمل کنه، می‌تونه روی اثربخشی واکسن‌ها در سطح جامعه یا چیزی که بهش میگن ایمنی جمعی اثر بذاره. به این مقاله نگاه کنید.

از طرف دیگه هم‌جوری در گروه‌های مختلف متفاوته. یعنی ممکنه توی یه مهمونی بزرگ، یه خانم و یه آقا زیاد ببینیم که با هم حرف می‌زنن دوتایی ولی وقتی گروه سه نفری تشکیل میشه، خانم‌ها بیشتر گروه‌هایی تشکیل میدن که هر سه نفرشون خانمه. پس بسته به اندازه گروه، میزان هموفویلی یا هم‌جوری می‌تونه متفاوت باشه.

مقاله جدید ما یک مدل ریاضی معرفی می‌کنه که به کمکش میشه شبکه‌هایی ساخت که توی گروه‌ها بسته به اندازه‌شون بشه هم‌جوری‌های متفاوتی رو لحاظ کرد. برای دیدن جزئیات فنی به این مقاله نگاه کنید.

Homophily Within and Across Groups

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@sitpor | sitpor.org
instagram.com/sitpor_media

Tips for scientific conference presentations

https://skullsinthestars.com/2024/12/03/my-tips-for-scientific-conference-presentations/

Probability Yardstick infographic

Anyone in the UK intelligence community using the term ‘likely’, for example, should mean a chance of between 55% and 75% (go.nature.com/3vhu5zc).

The best CS courses of the academic year 2023-2024 at Aalto University:

CS-E4580 Programming Parallel Computers, Jukka Suomela
CS-C1000 Introduction to Artificial Intelligence, Arno Solin
CS-E4890 Deep Learning, Alexander Ilin
CS-E4190 Cloud Software and Systems, Mario Di Francesco & Bo Zhao
CS-E4895 Gaussian Processes, Arno Solin

CS-E407517 Special Course in Machine Learning, Data Science and Artificial Intelligence: Seminar on NLP Research, Pekka Marttinen
CS-E5310 ICT Enabled Service Business and Innovation, Kari Hiekkanen
CS-E4910 Software Project 3, Jari Vanhanen & Casper Lassenius
CS-AJ0120 Modern and Emerging Programming Languages: Rust, Arto Hellas
CS-EJ3311 Deep Learning with Python, Alexander Jung

https://www.aalto.fi/en/department-of-computer-science

BSc or MSc degree student, come to work with us on summer 2025. The application form will be open from January 7th to January 31st 2025.

Choose your topic and apply!
https://www.aalto.fi/en/department-of-computer-science/summer-employee-positions-at-the-department-of-computer-science-2025

#Postdoc Research Assistant to join Oxford Martin School Programme on Digital Pandemic Preparedness working with Christl Donnelly & Moritz Kraemer — University of Oxford

https://iddjobs.org/jobs/postdoctoral-research-assistant-to-join-oxford-martin-school-programme-on-digital-pandemic-preparedness-working-with-christl-donnelly-moritz-kraemer

Position for two-year #postdoc with background in computer science interested in working with social scientists/historians to study social evolution through computational lens. Details here:

https://santafe.edu/about/jobs/postdoc-comp-complexity

3-year #PostDoc position centered on the extraction and analysis of relations of societal actors (e.g. politicians, CEOs, ...) from unstructured text data. Feel free to reach out if you want to know more! Deadline: Jan 1st.

https://jobs.uni-graz.at/en/jobs/a122f6d8-06bf-e5a2-6fba-67612e91fb27

Roadmap on machine learning glassy dynamics

Unravelling the connections between microscopic structure, emergent physical properties and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. We explore the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present both successful ML applications and open problems for the future, such as transferability and interpretability of ML approaches. To foster a collaborative community effort, we also highlight the ‘GlassBench’ dataset, which provides simulation data and benchmarks for both 2D and 3D glass formers. We compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. Our goal is to provide guidelines for the development of ML techniques in systems displaying slow dynamics and inspire new directions to improve our theoretical understanding of glassy liquids.

https://www.nature.com/articles/s42254-024-00791-4

Garbage in Garbage out: Impacts of data quality on criminal network intervention

https://arxiv.org/abs/2501.01508

Network Medicine Training Course

https://www.diag.uniroma1.it/network_medicine_training_course

How well prepared are we to rapidly analyse a new influenza pandemic? A brief perspective on analysis conducted for UK government advisory groups during COVID-19

https://epiverse-trace.github.io/posts/covid-analysis/