🚨 New paper in the Journal of Complex Networks "From chambers to echo chambers: quantifying polarization with a second-neighbor approach applied to Twitter’s climate discussion" Kudos to Fabián Aguirre & @SinniaData for the great collab! Read more 👉:

New #BlueSky post about our new ArXiv paper. Check it out: https://t.co/9oyEHXSsYG Arxiv: https://t.co/ynnTqlQbQi

cc @EddyRobinson

Updated #ArXiv version for those without access to the journal: https://t.co/XkBwGQxGSB Also, code and reproducible results available in the #Github repo: https://t.co/9DWpx3P5kM

Happy to see the new book on Formal Methods in Musicology out! I co-wrote a chapter with @TonatiuhMateo exploring rhythm and form in music using complex systems ideas. Big shoutout to Pablo Padilla & Francis Knights for putting it all together! 📘

Capitalists changes to whatever side is profitable lmao

🤖 Thanks also to chat-GPT for helping me make this thread. I usually suck at promoting my work, so this just made that process much easier. (11/11) Peace out! 🖖

🎓 Collaborators and Acknowledgments Big thanks to my collaborators @Enchufa2, Manuel Cebrian, and Rosa E. Lillo for their valuable contributions to this research. Together, we’re exploring new ways to leverage social networks for better outcomes. (10/11)

🌐 Relevance Beyond Recruitment This model bridges economic game theory, network diffusion models, and computational social sciences. It offers a framework to study task completion where incentives drive behavior—applicable to many fields beyond just recruitment. (9/11)

📊 Comparison: IHC vs. Centralized Systems When comparing the IHC to traditional, centralized models (like LinkedIn’s direct recommendations), the IHC consistently outperforms—especially in larger, decentralized networks, reaching people who might otherwise remain unseen. (8/11)

🧠 Real-World Applications The implications go beyond recruitment. The IHC model could be used to distribute incentives in other areas like volunteer coordination, knowledge sharing, or any task where participation across a network is critical. (7/11)

🔄 A Fairer Rewards Distribution Traditional job platforms focus on direct applications, benefiting only the successful candidate. The IHC model shares the reward with everyone in the recommendation chain, making the process more fair and collaborative. (6/11)

🤔 Unexpected Finding: Long Chains Even for jobs that require specific, niche skills, the IHC model performs well. By motivating people to pass the opportunity along, the model extends the reach deeper, leading to more successful hires where centralized systems struggle. (5/11)

🔍 Key Results: Boosting Success Rates Simulations show higher hiring success vs centralized methods --especially for highly specific positions. By encouraging peer-to-peer recommendations, more people are reached, increasing the likelihood of filling job vacancies. (4/11)

🎯 Beyond Filling Jobs: A Framework for Task Completion The model is not limited to recruitment. It provides a general framework for how tasks can be completed within a social network through economic incentives. Every recommendation counts. (3/11)

💼 A Social Approach to Recruitment In the IHC model, agents are incentivized to apply for a position or recommend it to their peers. If the job is filled, everyone gets a share of the reward. This turns the hiring process into a more inclusive, community-driven effort. (2/11)

📢 New Pre-print: Incentivized Network Dynamics in Digital Job Recruitment We introduce the Independent Halting Cascade (IHC) model, a fresh approach to job recruitment where everyone in a social network benefits—not just the final hire. See more!👇(1/11) https://t.co/c4TZTroY5i

🚨 We are happy to announce the 6th edition of the Complexity72h workshop, which will take place on June 23-27, 2025! 🚨 We are excited to return to Universidad Carlos III de Madrid, in Madrid, Spain @uc3m☀️ Stay tuned for more details!

🚀 ¡CALL FOR TALKS! 🚀 ¡Últimos días para apuntarse a 𝗔𝗜 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘀! Un evento imprescindible sobre Inteligencia Artificial y Datos, los próximos 17 y 18 de octubre en Madrid. 💼 Inscripción gratuita aquí ➡

Good news everyone! A new version of graph-tool is just out! @graph_tool https://t.co/fZTRrRruXD @graph_tool is a comprehensive and efficient Python library to work with networks, including structural, dynamical, and statistical algorithms, as well as visualization. 1/N