🔖 Correlations and dynamics of consumption patterns in social-economic networks
Yannick Leo, Márton Karsai, Carlos Sarraute, Eric Fleury
🔗 arxiv.org/pdf/1801.08856.pdf
📌 ABSTRACT
We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.
https://t.co/h2kl2pQhDV
Yannick Leo, Márton Karsai, Carlos Sarraute, Eric Fleury
🔗 arxiv.org/pdf/1801.08856.pdf
📌 ABSTRACT
We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.
https://t.co/h2kl2pQhDV
Twitter
Alessandro Vespignani
Correlations and dynamics of consumption patterns in social-economic networks https://t.co/ybrqLPYGFA
💡 Networks, Crowds, and Markets: Reasoning About a Highly Connected World
By David Easley and Jon Kleinberg
http://www.cs.cornell.edu/home/kleinber/networks-book/
By David Easley and Jon Kleinberg
http://www.cs.cornell.edu/home/kleinber/networks-book/
🔅 The Shallowness of Google Translate
The program uses state-of-the-art AI techniques, but simple tests show that it's a long way from real understanding.
https://www.theatlantic.com/amp/article/551570/
The program uses state-of-the-art AI techniques, but simple tests show that it's a long way from real understanding.
https://www.theatlantic.com/amp/article/551570/
Forwarded from انجمن فیزیک ایران (akram Mirhosseini)
🎞 Trent McConaghy at SFI describing how #blockchains relate to #complex_systems. Catch the livestream on our youtube channel:
https://www.youtube.com/watch?v=gB8oY5RPzxQ
https://www.youtube.com/watch?v=gB8oY5RPzxQ
YouTube
Tokens and Complex Systems
Seminar - January 31, 2018 - Trent McConaghy I will describe how complex systems relate to tokenized ecosystems, aka blockchains. I will describe AI DAOs (de...
⭕️ Fundamentals of Machine Learning
Lead instructor: Brendan Tracey and Artemy Kolchinsky
https://www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning
About the Tutorial:
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works.
In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.
Students of all levels should be able to follow this reasonably-paced introduction to one of the most important engineering breakthroughs of our time.
High quality videos:
🎞 https://www.aparat.com/video/video/listuser/username/carimi/usercat/110284
Lead instructor: Brendan Tracey and Artemy Kolchinsky
https://www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning
About the Tutorial:
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works.
In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.
Students of all levels should be able to follow this reasonably-paced introduction to one of the most important engineering breakthroughs of our time.
High quality videos:
🎞 https://www.aparat.com/video/video/listuser/username/carimi/usercat/110284
⭕️ Introduction to Computation Theory
Lead instructor: Josh Grochow
https://www.complexityexplorer.org/courses/58-introduction-to-computation-theory
About the Tutorial:
Introduction to Computation Theory is an overview of some basic principles of computation and computational complexity, with an eye towards things that might actually be useful without becoming a researcher. Students will examine the formal mathematics for foundational computation proofs, as well as gain tools to analyze hard computational problems themselves.
Students who take this course should have basic knowledge of the principles of graphs. Some tutorial material references linear algebra, but familiarity is not necessary. This tutorial uses proofs, and requires understandings of formal math notations.
High quality videos:
🎞 https://www.aparat.com/video/profile/one/usercat/110290/username/carimi
Lead instructor: Josh Grochow
https://www.complexityexplorer.org/courses/58-introduction-to-computation-theory
About the Tutorial:
Introduction to Computation Theory is an overview of some basic principles of computation and computational complexity, with an eye towards things that might actually be useful without becoming a researcher. Students will examine the formal mathematics for foundational computation proofs, as well as gain tools to analyze hard computational problems themselves.
Students who take this course should have basic knowledge of the principles of graphs. Some tutorial material references linear algebra, but familiarity is not necessary. This tutorial uses proofs, and requires understandings of formal math notations.
High quality videos:
🎞 https://www.aparat.com/video/profile/one/usercat/110290/username/carimi
🔹 The central nervous system of an octopus, and the central government of a nation: parallel examples of the emergence of collective behavior.
http://crookedtimber.org/2018/02/01/the-birth-of-intermediacy/
http://crookedtimber.org/2018/02/01/the-birth-of-intermediacy/
Crooked Timber
The Birth of Intermediacy?
I’m taking a break from reading stuff about political theory and liberalism and reading, instead, Other Minds: The Octopus, the Sea, and the Deep Origins of Consciousness [amazon]. It turns o…
🔰 Critical response | In this month's Thesis, Mark Buchanan surveys recent developments in our understanding of criticality in biological systems:
#Nature
https://t.co/i690D0FILT
#Nature
https://t.co/i690D0FILT
Forwarded from انجمن علمی شناسا
📈آیا با ریاضیات میتوان رفتارهای اجتماعی را پیشبینی کرد؟
🗣دکتر میثم علیزاده
🗣دکتر عبدالحسین کلانتری
⏱دوشنبه ۱۶بهمن ساعت ۱۶ تا ۲۰
🏛دانشکده فیزیک شریف تالار دکتر جناب
@Shenasa_SUT
@Hamband_Math_CS
🗣دکتر میثم علیزاده
🗣دکتر عبدالحسین کلانتری
⏱دوشنبه ۱۶بهمن ساعت ۱۶ تا ۲۰
🏛دانشکده فیزیک شریف تالار دکتر جناب
@Shenasa_SUT
@Hamband_Math_CS
Forwarded from JafaRiLab (Mohieddin Jafari)
#از_R_لذت_ببریم
دوره ی آموزشی ۳ روزه ی تصویرسازی حرفه ای در علم داده با استفاده از زبان R و پکیج ggplot2
لینک ثبت نام:
https://evand.com/events/rggplot2
@jafarilab
دوره ی آموزشی ۳ روزه ی تصویرسازی حرفه ای در علم داده با استفاده از زبان R و پکیج ggplot2
لینک ثبت نام:
https://evand.com/events/rggplot2
@jafarilab