✅ Recently posted a paper with @erikphoel [https://t.co/Til7g83e2U] about effective information and causal emergence in networks.
Here's the python code used in the paper, which I've tried to release in a tutorial-esque format [https://t.co/XgH2mRKaL9].
A thread: https://t.co/z823SjEckL
Here's the python code used in the paper, which I've tried to release in a tutorial-esque format [https://t.co/XgH2mRKaL9].
A thread: https://t.co/z823SjEckL
arXiv.org
Uncertainty and causal emergence in complex networks
The connectivity of a network conveys information about the dependencies
between nodes. We show that this information can be analyzed by measuring the
uncertainty (and certainty) contained in...
between nodes. We show that this information can be analyzed by measuring the
uncertainty (and certainty) contained in...
💶Social inequality: from data to statistical physics modeling
Arnab Chatterjee, Asim Ghosh, Jun-ichi Inoue, Bikas K. Chakrabarti
(Submitted on 9 Jul 2015)
https://arxiv.org/pdf/1507.02445
Social inequality is a topic of interest since ages, and has attracted researchers across disciplines to ponder over it origin, manifestation, characteristics, consequences, and finally, the question of how to cope with it. It is manifested across different strata of human existence, and is quantified in several ways. In this review we discuss the origins of social inequality, the historical and commonly used non-entropic measures such as Lorenz curve, Gini index and the recently introduced k index. We also discuss some analytical tools that aid in understanding and characterizing them. Finally, we argue how statistical physics modeling helps in reproducing the results and interpreting them.
Arnab Chatterjee, Asim Ghosh, Jun-ichi Inoue, Bikas K. Chakrabarti
(Submitted on 9 Jul 2015)
https://arxiv.org/pdf/1507.02445
Social inequality is a topic of interest since ages, and has attracted researchers across disciplines to ponder over it origin, manifestation, characteristics, consequences, and finally, the question of how to cope with it. It is manifested across different strata of human existence, and is quantified in several ways. In this review we discuss the origins of social inequality, the historical and commonly used non-entropic measures such as Lorenz curve, Gini index and the recently introduced k index. We also discuss some analytical tools that aid in understanding and characterizing them. Finally, we argue how statistical physics modeling helps in reproducing the results and interpreting them.
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی_محتوا
«خوشهیابی در شبکههای پیچیده»
🗣 دانیال پاپی - دانشگاه شهید بهشتی
🎞 https://www.aparat.com/v/j39py
~~~~~~~~~~~~~~~~~
🔗 سخنرانیهای بیشتر در:
https://ccnsd.ir/events-news/weekly-seminars/
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
~~~~~~~~~~~~~~~~~
«خوشهیابی در شبکههای پیچیده»
🗣 دانیال پاپی - دانشگاه شهید بهشتی
🎞 https://www.aparat.com/v/j39py
~~~~~~~~~~~~~~~~~
🔗 سخنرانیهای بیشتر در:
https://ccnsd.ir/events-news/weekly-seminars/
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
~~~~~~~~~~~~~~~~~
آپارات - سرویس اشتراک ویدیو
خوشهیابی در شبکههای پیچیده - دانیال پاپی
Clustering in Complex Networks by Danial Papi
Graph centrality is a question of scale
“connection between diffusion and geometry to introduce a multiscale centrality measure”
https://t.co/LAlh4Sl0h8
“connection between diffusion and geometry to introduce a multiscale centrality measure”
https://t.co/LAlh4Sl0h8
Fundamental Structures in Dynamic Communication Networks
“framework for how to analyze networks in general, rather than a particular result of analyzing a particular dataset”
https://t.co/jc8odqop6x
“framework for how to analyze networks in general, rather than a particular result of analyzing a particular dataset”
https://t.co/jc8odqop6x
✔ https://blog.stephenwolfram.com/2019/07/mitchell-feigenbaum-1944-2019-4-66920160910299067185320382/
Stephenwolfram
Mitchell Feigenbaum (1944‑2019), 4.66920160910299067185320382…—Stephen Wolfram Writings
Stephen Wolfram shares his memories of mathematical physicist Mitchell Feigenbaum. Also a detailed discussion of his work and big discovery of a universal constant for functions approaching chaos via period doubling.
Sitpor.org سیتپـــــور
نگاهی به کتاب «فرمول: قوانین جهانشمول موفقیت» باراباشی http://www.sitpor.org/2019/07/the-formula/ آلبرت لازلو باراباشی، یک دانشمند شبکه معروفه که اخیرا پروژهای به اسم «علم موفقیت» در دپارتمان «علم شبکه» دانشگاه نورثایسترن شروع کرده. منظور از علم موفقیت،…
🎞 https://www.ted.com/talks/albert_laszlo_barabasi_when_in_life_are_you_most_likely_to_succeed/up-next
Ted
Albert-László Barabási: The real relationship between your age and your chance of success
Backed by mathematical analysis, network theorist Albert-László Barabási explores the hidden mechanisms that drive success -- no matter your field -- and uncovers an intriguing connection between your age and your chance of making it big.
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
«مقدمهای بر بازبهنجارش - قسمت اول»
🗣 عباس کریمی - دانشگاه شهید بهشتی
⏰ دوشنبه، ۷ مرداد - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
به امید دیدار
برای هماهنگی و اطلاعات بیشتر:
@herman1
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
«مقدمهای بر بازبهنجارش - قسمت اول»
🗣 عباس کریمی - دانشگاه شهید بهشتی
⏰ دوشنبه، ۷ مرداد - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
به امید دیدار
برای هماهنگی و اطلاعات بیشتر:
@herman1
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
In this free online course from MIT, learn methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest. Data Analysis for Social Scientists starts on Wednesday, September 3 – enroll today!
https://t.co/EYq6PeUhLh
https://t.co/EYq6PeUhLh
New review on
Explosive Phenomena in Complex Networks
“review the vast literature on explosive phenomena and synthesize the fundamental connections between models and survey the application areas”
https://t.co/RFPn7XSmqZ
Explosive Phenomena in Complex Networks
“review the vast literature on explosive phenomena and synthesize the fundamental connections between models and survey the application areas”
https://t.co/RFPn7XSmqZ
💰 Economic data science
ML and AI + Complex Systems and Network Science to help decode and manage the digital transformation. Looking forward to new partnerships and collaborations.
How can new kinds of data allow us to tackle the full complexity of the socio-technical systems we create and inhabit?
https://t.co/GhDepWHGBy
ML and AI + Complex Systems and Network Science to help decode and manage the digital transformation. Looking forward to new partnerships and collaborations.
How can new kinds of data allow us to tackle the full complexity of the socio-technical systems we create and inhabit?
https://t.co/GhDepWHGBy
A study of spider colonies supports a controversial idea in evolution — that natural selection can act on communities as well as on individuals.
https://t.co/Mu4etwLG2q
https://t.co/Mu4etwLG2q
Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks
in @PLOSCompBiol
https://t.co/q2D9wDZw3z
in @PLOSCompBiol
https://t.co/q2D9wDZw3z
مدرسه تابستانه نظریه بازی ها
9-14 شهریور 1398
پژوهشکده علوم زیستی
لینک:
http://bs.ipm.ac.ir/game2019/index.jsp
9-14 شهریور 1398
پژوهشکده علوم زیستی
لینک:
http://bs.ipm.ac.ir/game2019/index.jsp
Challenges in Community Discovery on Temporal Networks
“dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature”
https://t.co/sVcUsvlVtr
“dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature”
https://t.co/sVcUsvlVtr
Beyond the Coverage of Information Spreading: Analytical and Empirical Evidence of Re-exposure in Large-scale Online Social Networks
“analyzing trending news on Sina Weibo (China's Twitter) with 430 million connected users“
https://t.co/YzJ6AIX7Co
“analyzing trending news on Sina Weibo (China's Twitter) with 430 million connected users“
https://t.co/YzJ6AIX7Co
Networks of Power: Analyzing World Leaders Interactions on Social Media
“cross-national dataset of Twitter communication for leaders of 193 countries for the period of 2012-2017”
https://t.co/097SLCdjXC
“cross-national dataset of Twitter communication for leaders of 193 countries for the period of 2012-2017”
https://t.co/097SLCdjXC