🌀 Inferential Statistics
https://www.coursera.org/learn/inferential-statistics-intro
📌 About this course:
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
https://www.coursera.org/learn/inferential-statistics-intro
📌 About this course:
This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
🌀 Applied Social Network Analysis in Python
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
https://www.coursera.org/learn/python-social-network-analysis
📌 About this course:
This course will introduce the learner to network modelling through the #networkx toolset. Used to model knowledge graphs and physical and virtual #networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (#graph_theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and #visualize networks. The third week will describe #metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the #social networking analysis workflow, from problem identification through to generation of insight.
💡A brief history of deep learning, including the rise, fall, and renaissance of neural networks:
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Clear, informative article on #neural_networks and #deep_learning, with lots of good historical background and insights.
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
Clear, informative article on #neural_networks and #deep_learning, with lots of good historical background and insights.
MIT News
Explained: Neural networks
“Deep learning,” the machine-learning technique behind the best-performing artificial-intelligence systems of the past decade, is really a revival of the 70-year-old concept of neural networks.
🗞 Modeling interactions between political parties and electors
F. Bagarello, F. Gargano
🔗 https://arxiv.org/abs/1704.05453
📌 ABSTRACT
In this paper we extend some recent results on an operatorial approach to the description of alliances between political parties interacting among themselves and with a basin of electors. In particular, we propose and compare three different models, deducing the dynamics of their related {\em decision functions}, i.e. the attitude of each party to form or not an alliance. In the first model the interactions between each party and their electors are considered. We show that these interactions drive the decision functions towards certain asymptotic values depending on the electors only: this is the {\em perfect party}, which behaves following the electors' suggestions. The second model is an extension of the first one in which we include a rule which modifies the status of the electors, and of the decision functions as a consequence, at some specific time step. In the third model we neglect the interactions with the electors while we consider cubic and quartic interactions between the parties and we show that we get (slightly oscillating) asymptotic values for the decision functions, close to their initial values. This is the {\em real party}, which does not listen to the electors. Several explicit situations are considered in details and numerical results are also shown.
F. Bagarello, F. Gargano
🔗 https://arxiv.org/abs/1704.05453
📌 ABSTRACT
In this paper we extend some recent results on an operatorial approach to the description of alliances between political parties interacting among themselves and with a basin of electors. In particular, we propose and compare three different models, deducing the dynamics of their related {\em decision functions}, i.e. the attitude of each party to form or not an alliance. In the first model the interactions between each party and their electors are considered. We show that these interactions drive the decision functions towards certain asymptotic values depending on the electors only: this is the {\em perfect party}, which behaves following the electors' suggestions. The second model is an extension of the first one in which we include a rule which modifies the status of the electors, and of the decision functions as a consequence, at some specific time step. In the third model we neglect the interactions with the electors while we consider cubic and quartic interactions between the parties and we show that we get (slightly oscillating) asymptotic values for the decision functions, close to their initial values. This is the {\em real party}, which does not listen to the electors. Several explicit situations are considered in details and numerical results are also shown.
Evolutionary dynamics on any population structure
http://www.nature.com/nature/journal/v544/n7649/full/nature21723.html
http://www.nature.com/nature/journal/v544/n7649/full/nature21723.html
#سمینارهای_هفتگی گروه سیستمهای پیچیده و علم شبکه دانشگاه شهید بهشتی
🔹دوشنبه، ۰۴ اردیبهشت ماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
🔹دوشنبه، ۰۴ اردیبهشت ماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
💭 Albert-László Barabási on the diversity of networks
http://physicstoday.scitation.org/do/10.1063/PT.5.3050/full/
http://physicstoday.scitation.org/do/10.1063/PT.5.3050/full/
ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks. | bioRxiv
http://biorxiv.org/content/early/2017/03/23/119099
http://biorxiv.org/content/early/2017/03/23/119099
bioRxiv
ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.
The identification of disease associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. A major problem hampering their identification is the…
🗞 The Emergence of Consensus
Andrea Baronchelli
🔗 https://arxiv.org/pdf/1704.07767
📌 ABSTRACT
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. This paper overviews the main dimensions over which the debate has unfolded and discusses some representative results, with a focus on those situations in which consensus emerges `spontaneously' in absence of centralised institutions. Covered topics include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
Andrea Baronchelli
🔗 https://arxiv.org/pdf/1704.07767
📌 ABSTRACT
The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. This paper overviews the main dimensions over which the debate has unfolded and discusses some representative results, with a focus on those situations in which consensus emerges `spontaneously' in absence of centralised institutions. Covered topics include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
🌀 The Problem of Action at a Distance in Networks and the Emergence of Preferential Attachment from Triadic Closure
Jérôme Kunegis, Fariba Karimi, Jun Sun
🔗 https://arxiv.org/pdf/1408.0119
(Submitted on 1 Aug 2014 (v1), last revised 24 Apr 2017 (this version, v2))
📌 ABSTRACT
In this paper, we characterise the notion of preferential attachment in networks as action at a distance, and argue that it can only be an emergent phenomenon -- the actual mechanism by which networks grow always being the closing of triangles. After a review of the concepts of triangle closing and preferential attachment, we present our argument, as well as a simplified model in which preferential attachment can be derived mathematically from triangle closing. Additionally, we perform experiments on synthetic graphs to demonstrate the emergence of preferential attachment in graph growth models based only on triangle closing.
Jérôme Kunegis, Fariba Karimi, Jun Sun
🔗 https://arxiv.org/pdf/1408.0119
(Submitted on 1 Aug 2014 (v1), last revised 24 Apr 2017 (this version, v2))
📌 ABSTRACT
In this paper, we characterise the notion of preferential attachment in networks as action at a distance, and argue that it can only be an emergent phenomenon -- the actual mechanism by which networks grow always being the closing of triangles. After a review of the concepts of triangle closing and preferential attachment, we present our argument, as well as a simplified model in which preferential attachment can be derived mathematically from triangle closing. Additionally, we perform experiments on synthetic graphs to demonstrate the emergence of preferential attachment in graph growth models based only on triangle closing.
🌀 Deadline coming up!
Calling all young quantitative biologists/biophysicists:
there are post-doctoral positions open in ICTP's Quantitative Life Science section.
🔥 Apply by 30 April!
http://ictp.it/wqbq4
Calling all young quantitative biologists/biophysicists:
there are post-doctoral positions open in ICTP's Quantitative Life Science section.
🔥 Apply by 30 April!
http://ictp.it/wqbq4