🌀 یه مدل ساده شده از دینامیک عقاید مختلف توی جامعه:
https://www.complexity-explorables.org/explorables/loyale-with-cheese/
تو مرورگرتون میتونید پارامترها رو تغییر بدید و نتایج رو ببینید.
https://www.complexity-explorables.org/explorables/loyale-with-cheese/
تو مرورگرتون میتونید پارامترها رو تغییر بدید و نتایج رو ببینید.
www.complexity-explorables.org
Echo Chambers
A dynamic network that explains the emergence of groups of uniform opinion
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
🔗 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
R-bloggers
Machine Learning Explained: Dimensionality Reduction
Dealing with a lot of dimensions can be painful for machine learning algorithms. High dimensionality will increase the computational complexity, increase the risk of overfitting (as your algorithm has more degrees of freedom) and the sparsity of the data…
Complex Systems Studies
Photo
Awesome set of #DataScience #MachineLearning graphics from #100DaysOfMLCode:
🔗 https://t.co/BQpyn75uQ6
#BigData #DataScientists #Algorithms #Coding #Python
🔗 https://t.co/BQpyn75uQ6
#BigData #DataScientists #Algorithms #Coding #Python
GitHub
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding. Contribute to Avik-Jain/100-Days-Of-ML-Code development by creating an account on GitHub.
🔖 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.
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.
🌀 A visual introduction to machine learning, Part II https://t.co/KtgciEYr5H #AI
#DeepLearning #MachineLearning #DataScience
#DeepLearning #MachineLearning #DataScience
www.r2d3.us
A visual introduction to machine learning, Part II
Learn about bias and variance in our second animated data visualization.
Forwarded from Sitpor.org سیتپـــــور
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!
🔗 https://www.sitpor.org/2016/06/bachelor-of-science-in-physics/
📢 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!
🔗 https://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
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
Open source software project for visualizing graphs and networks in LaTeX.
https://arxiv.org/abs/1709.06005v2
Complex Systems Studies pinned «🙌 صفحه اینستاگرام ما: 🔗 https://www.instagram.com/complex_systems/»
🔥 هر کی از دانشگاه رفت، معنیش این نیست که شکست خورده:
https://www.nature.com/articles/d41586-018-05838-y?utm_source=twt_nnc&utm_medium=social&utm_campaign=naturenews&sf194766288=1
https://www.nature.com/articles/d41586-018-05838-y?utm_source=twt_nnc&utm_medium=social&utm_campaign=naturenews&sf194766288=1
Nature
Why it is not a ‘failure’ to leave academia
Here’s how PhD students can prepare for different careers, and how lab heads can help.
🔖 Recent advances of percolation theory in complex networks
Deokjae Lee, Y. S. Cho, K.-I. Goh, D.-S. Lee, B. Kahng
🔗 https://arxiv.org/pdf/1808.00905.pdf
📌 ABSTRACT
During the past two decades, percolation has long served as a basic paradigm for network resilience, community formation and so on in complex systems. While the percolation transition is known as one of the most robust continuous transitions, the percolation transitions occurring in complex systems are often of different types such as discontinuous, hybrid, and infinite-order phase transitions. Thus, percolation has received considerable attention in network science community. Here we present a very brief review of percolation theory recently developed, which includes those types of phase transitions, critical phenomena, and finite-size scaling theory. Moreover, we discuss potential applications of theoretical results and several open questions including universal behaviors.
Deokjae Lee, Y. S. Cho, K.-I. Goh, D.-S. Lee, B. Kahng
🔗 https://arxiv.org/pdf/1808.00905.pdf
📌 ABSTRACT
During the past two decades, percolation has long served as a basic paradigm for network resilience, community formation and so on in complex systems. While the percolation transition is known as one of the most robust continuous transitions, the percolation transitions occurring in complex systems are often of different types such as discontinuous, hybrid, and infinite-order phase transitions. Thus, percolation has received considerable attention in network science community. Here we present a very brief review of percolation theory recently developed, which includes those types of phase transitions, critical phenomena, and finite-size scaling theory. Moreover, we discuss potential applications of theoretical results and several open questions including universal behaviors.
سومین گردهمایی پیوند
📅 سهشنبه ۱۶ مرداد
⏰ ۹ تا ۱۸
📌 ثبتنام: peyvand.me
🍕 درصورتی که به قسمتی از همایش علاقمند هستید میتوانید از #کد_تخفیف ۶۰ درصدی استفاده کنید:
nolaunch
@peyvandgathering
📅 سهشنبه ۱۶ مرداد
⏰ ۹ تا ۱۸
📌 ثبتنام: peyvand.me
🍕 درصورتی که به قسمتی از همایش علاقمند هستید میتوانید از #کد_تخفیف ۶۰ درصدی استفاده کنید:
nolaunch
@peyvandgathering