Here is the paper presented by Barabasi at #ICCS2018:
Controllability of complex networks
https://t.co/3YklaJm78N

Social Networks and the Intention to Migrate
CID Research Fellow & Graduate Student Working Paper No. 90

Miriam Manchin and Sultan Orazbayev
March 2018

Abstract:

Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals' intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we nd that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally. https://www.hks.harvard.edu/centers/cid/publications/fellow-graduate-student-working-papers/social-networks-migration

🧓🏼 http://sappingattention.blogspot.com/2018/07/mea-culpa-there-is-crisis-in-humanities.html?m=1

A billiard ball in a rectangle with two circular ends: slight changes in initial conditions make such a big difference that eventually it could be anywhere.
To be precise, we say its motion is ergodic

📌 http://math.ucr.edu/home/baez/diary/november_2016.html#november_15

Scholarpedia article by Lyonia Bunimovich on 'dynamical billiards': https://t.co/TK6wnPgHgd

🌀 ‏یه مدل ساده‌ شده از دینامیک عقاید مختلف توی جامعه:
http://www.complexity-explorables.org/explorables/loyale-with-cheese/

تو مرورگرتون می‌تونید پارامترها رو تغییر بدید و نتایج رو ببینید.

Slides for #JSM2018 talk on networks and complex data available at https://t.co/PDbaFit5Ej

🔅Machine Learning Explained: Dimensionality Reduction:
🔗 https://t.co/tk4IPArpCZ


🔅Data Science with Python & R: Dimensionality Reduction and Clustering:
🔗 https://www.codementor.io/jadianes/data-science-python-pandas-r-dimensionality-reduction-du1081aka

Awesome set of #DataScience #MachineLearning graphics from #100DaysOfMLCode:
🔗 https://t.co/BQpyn75uQ6

#BigData #DataScientists #Algorithms #Coding #Python

🔖 DeepLink: A Novel Link Prediction Framework based on Deep Learning

Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour

🔗 https://arxiv.org/pdf/1807.10494.pdf

📌 ABSTRACT
Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.

🌀 https://www.wsj.com/amp/articles/BL-CIOB-14235?responsive=y#click=https://t.co/5shCQBkwjx

🌀 A visual introduction to machine learning, Part II https://t.co/KtgciEYr5H #AI

#DeepLearning #MachineLearning #DataScience

برسانید به دست کسانی که کنکور کارشناسی داده‌اند:

📢 چهارسال فیزیک!

چهارسال گذشت و دوره کارشناسی فیزیک من تموم شد. چهارسال پر از فراز و نشیبی که با تمام لذت‌ها و هیجان‌ها، سختی‌ها و فشارها بالاخره به پایان رسید (من ورودی ۹۱ فیزیک دانشگاه شهیدبهشتی بودم). قصد دارم طی این نوشته، تجربه‌های خودم از دوران کارشناسی فیزیک رو بنویسم. امیدوارم این نوشته‌ برای کسایی که قصد دارن فیزیک رو به صورت آکادمیک شروع کنن و برای کسانی که به تازگی وارد فیزیک شدن مفید واقع بشه!

🔗 http://www.sitpor.org/2016/06/bachelor-of-science-in-physics/

🌐 "The atom is the icon of the 20th century. The atom whirls alone. It is the metaphor for individuality. But the atom is the past. The symbol for the next century is the net. The net has no center, no orbits, no certainty. It is an indefinite web of causes. The net is the archetype displayed to represent all circuits, all intelligence, all interdependence, all things economic, social, or ecological, all communications, all democracy, all families, all large systems, almost all that we find interesting and important. Whereas the atom represents clean simplicity, the net channels messy complexity.
The net is our future.
Of all the endeavors we humans are now engaged in, perhaps the grandest of them all is the steady weaving together of our lives, minds, and artifacts into a global scale network. This great work has been going on for decades, but recently our ability to connect has accelerated. Two brand-new technological achievements?the silicon chip and the silicate glass fiber?have rammed together with incredible speed. Like nuclear particles crashing together in a cyclotron, the intersection of these two innovations has unleashed a never-before-seen force: the power of a pervasive net. As this grand net spreads, an animated swarm is reticulating the surface of the planet. We are clothing the globe with a network society.
The dynamic of our society, and particularly our new economy, will increasingly obey the logic of networks. Understanding how networks work will be the key to understanding how the economy works."

Kevin Kelly

🌀 New version of the TikZ-network manual
Open source software project for visualizing graphs and networks in LaTeX.

https://arxiv.org/abs/1709.06005v2