๐นScale Free Complex Networks
๐ฅFree recorded tutorial from Albert-Lรกszlรณ Barabรกsi as the author of the best-seller book, Linked: The New Science of Networks.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#tutorial #video
๐ฅFree recorded tutorial from Albert-Lรกszlรณ Barabรกsi as the author of the best-seller book, Linked: The New Science of Networks.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#tutorial #video
YouTube
Scale Free Complex Networks
You might know Albert-Lรกszlรณ Barabรกsi as the author of the best-seller book, Linked: The New Science of Networks. Professor Barabรกsi's seminal work led to the understanding of the common structure of diverse complex systems: natural, technological, and social.โฆ
๐ Use of Python for Complex Network Analysis
๐ฅFree recorded tutorial from Andre Voigt who is a PhD candidate in Eivind Almaas' group at NTNU
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅFree recorded tutorial from Andre Voigt who is a PhD candidate in Eivind Almaas' group at NTNU
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Use of Python for Complex Network Analysis
The lecture and scripts used in this video can be found on our website: www.virtualsimlab.com
Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about theirโฆ
Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about theirโฆ
๐ Network Analysis Made Simple | Scipy 2019 Tutorial | Eric Ma
๐ฅFree recorded tutorial
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅFree recorded tutorial
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Network Analysis Made Simple | Scipy 2019 Tutorial | Eric Ma
Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasetsโฆ
๐Network analysis of protein interaction data: an introduction
๐ฅGood introductory document from EBI
๐ Study the tutorial
๐ฒChannel: @ComplexNetworkAnalysis
#tutorial
๐ฅGood introductory document from EBI
๐ Study the tutorial
๐ฒChannel: @ComplexNetworkAnalysis
#tutorial
๐ Social Network Analysis
๐ฅThis free recorded tutorial is an overview of social networks and social network analysis.
๐ฝWatch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅThis free recorded tutorial is an overview of social networks and social network analysis.
๐ฝWatch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Social Network Analysis
An overview of social networks and social network analysis.
See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
๐ Gephi Tutorial on Network Visualization and Analysis
๐ฅThis free recorded tutorial goes from import through the whole analysis phase for a citation network.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial #gephi
๐ฅThis free recorded tutorial goes from import through the whole analysis phase for a citation network.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial #gephi
YouTube
Gephi Tutorial on Network Visualization and Analysis
This tutorial goes from import through the whole analysis phase for a citation network. Data can be accessed at http://www.cs.umd.edu/~golbeck/INST633o/Viz.shtml
๐ Emergence of echo chambers and polarization dynamics in social networks
๐ฅEcho chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. In this talk, we will first review empirical evidence for the presence of echo chambers across social media platforms, by performing a comparative analysis among Gab, Facebook, Reddit, and Twitter. Then, we will present a simple modeling framework able to reproduce the observed opinion segregation in the social network. We consider networked agents characterized by heterogeneous activities and homophily, whose opinions can be reinforced by interactions with like-minded peers. We show that the transition between a global consensus and emerging polarized states in the network can be analytically characterized as a function of the social influence of the agents and the controversialness of the topic discussed. Finally, we consider a generalization to multiple opinions with respect to different topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we frame this problem in a formalism in which opinions evolve in a multidimensional space where topics form a non-orthogonal basis. We show that this approach can reproduce the correlations between extreme opinions on different topics found in survey data.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅEcho chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. In this talk, we will first review empirical evidence for the presence of echo chambers across social media platforms, by performing a comparative analysis among Gab, Facebook, Reddit, and Twitter. Then, we will present a simple modeling framework able to reproduce the observed opinion segregation in the social network. We consider networked agents characterized by heterogeneous activities and homophily, whose opinions can be reinforced by interactions with like-minded peers. We show that the transition between a global consensus and emerging polarized states in the network can be analytically characterized as a function of the social influence of the agents and the controversialness of the topic discussed. Finally, we consider a generalization to multiple opinions with respect to different topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we frame this problem in a formalism in which opinions evolve in a multidimensional space where topics form a non-orthogonal basis. We show that this approach can reproduce the correlations between extreme opinions on different topics found in survey data.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Emergence of echo chambers and polarization dynamics in social networks - Michele Starnini
Emergence of echo chambers and polarization dynamics in social networks
Abstract: Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact onโฆ
Abstract: Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact onโฆ
๐ Order and Disorder in Network Science
๐ฅA recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopic scale. Reversely, scientists have been fascinated by the emergence of spatial and temporal patterns in interacting systems. In this talk, I will give a personal view on these two sides within the field of network science, whose combination of order and randomness is at the core of several works on network dynamics and algorithms.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅA recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopic scale. Reversely, scientists have been fascinated by the emergence of spatial and temporal patterns in interacting systems. In this talk, I will give a personal view on these two sides within the field of network science, whose combination of order and randomness is at the core of several works on network dynamics and algorithms.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Order and Disorder in Network Science - Renaud Lambiotte
A recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopicโฆ
๐Machine Learning in Network Centrality Measures: Tutorial and Outlook
๐Journal: ACM Computing Surveys (I.F=10.282)
๐Publish year: 2019
๐ Study paper
๐ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #centrality
๐Journal: ACM Computing Surveys (I.F=10.282)
๐Publish year: 2019
๐ Study paper
๐ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #centrality
๐ Introduction to Data Science - NetworkX Tutorial
๐ฅFree recorded tutorial.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅFree recorded tutorial.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Introduction to Data Science - NetworkX Tutorial
Link to GitHub: https://github.com/sepinouda/Intro_to_Data_Science/tree/main/Lecture%204/Network%20Analysis
Linke to NetworkX Tutorials: https://networkx.org/documentation/stable/tutorial.html
Link to Gephi: https://gephi.org
Linke to NetworkX Tutorials: https://networkx.org/documentation/stable/tutorial.html
Link to Gephi: https://gephi.org
๐1
๐ Social network analysis: Considerations for data collection and analysis
๐ฅFree recorded tutorial.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
๐ฅFree recorded tutorial.
๐ฝ Watch
๐ฑChannel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Social network analysis: Considerations for data collection and analysis
Bernie Hogan completed his BA(hons) at the Memorial University of Newfoundland in Canada, where he received the University Medal in Sociology. Since then he has been working on Internet use and social networks at the University of Toronto under social networkโฆ
๐A tutorial on modeling and analysis of dynamic social networks. Part I
๐Journal: Annual Reviews in Control (I.F=10.699)
๐Publish year: 2017
๐Study paper
๐ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #dynamic
๐Journal: Annual Reviews in Control (I.F=10.699)
๐Publish year: 2017
๐Study paper
๐ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #dynamic
๐GEPHI โ Introduction to Network Analysis and Visualization
๐ฅFree online tutorial and recorded course
๐ฅNetwork Analysis and visualization appears to be an interesting tool to give the researcher the ability to see its data from a new angle. Because Gephi is an easy access and powerful network analysis tool, we propose a tutorial designed to allow everyone to make his first experiments on two complementary datasets.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Gephi #tutorial
๐ฅFree online tutorial and recorded course
๐ฅNetwork Analysis and visualization appears to be an interesting tool to give the researcher the ability to see its data from a new angle. Because Gephi is an easy access and powerful network analysis tool, we propose a tutorial designed to allow everyone to make his first experiments on two complementary datasets.
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Gephi #tutorial
www.martingrandjean.ch
GEPHI โ Introduction to Network Analysis and Visualization [new video] | Martin Grandjean
Network Analysis and visualization appears to be an interesting tool to give the researcher the ability to see its data from a new angle. Because Gephi is an easy access and powerful network analysis tool, we propose a tutorial designed to allow everyoneโฆ
๐1
๐ Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
๐ฅFree recorded tutorial by Avkash Chauhan.
๐ฅThis tutorial is part one of a two parts GNN series. Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving various machine learning problems where CNN or convolutional neural networks can not be applied. Then You will learn GNN technical details along with hands on exercise using Python programming along with NetworkX, PyG (pytorch_geometric) , matplotlib libraries.
๐ฝ Watch: part1 part2
๐ป Code
๐ Slides
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial #Graph #GNN #Python #NetworkX #PyG
๐ฅFree recorded tutorial by Avkash Chauhan.
๐ฅThis tutorial is part one of a two parts GNN series. Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving various machine learning problems where CNN or convolutional neural networks can not be applied. Then You will learn GNN technical details along with hands on exercise using Python programming along with NetworkX, PyG (pytorch_geometric) , matplotlib libraries.
๐ฝ Watch: part1 part2
๐ป Code
๐ Slides
๐ฒChannel: @ComplexNetworkAnalysis
#video #tutorial #Graph #GNN #Python #NetworkX #PyG
YouTube
Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
[Graph Neural Networks part 1/2]: This tutorial is part one of a two parts GNN series.
Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving variousโฆ
Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving variousโฆ
๐ A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
๐ฅFree recorded tutorial by Andre M. Bastos
๐นThis tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, I will review metrics for functional connectivity, including coherence, phase synchronization, phase slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition Next, I will highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a series of MATLAB-scripts, which can be executed by the tutorial participants to simulate each of these potential problems. I will discuss how some of these issues can be addressed using current methods
๐ฝWatch
๐ฑChannel: @ComplexNetworkAnalysis
#video #Tutorial #Connectivity #review
๐ฅFree recorded tutorial by Andre M. Bastos
๐นThis tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, I will review metrics for functional connectivity, including coherence, phase synchronization, phase slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition Next, I will highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a series of MATLAB-scripts, which can be executed by the tutorial participants to simulate each of these potential problems. I will discuss how some of these issues can be addressed using current methods
๐ฝWatch
๐ฑChannel: @ComplexNetworkAnalysis
#video #Tutorial #Connectivity #review
YouTube
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
Andre M. Bastos - MIT
Description: Oscillatory neuronal synchronization has been hypothesized to provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantagesโฆ
Description: Oscillatory neuronal synchronization has been hypothesized to provide a mechanism for dynamic network coordination. Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own advantagesโฆ
๐ Network Analysis Tutorial: Introduction to Networks
๐ฅFree recorded Tutorial by Eric Ma
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Tutorial #Networks
๐ฅFree recorded Tutorial by Eric Ma
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Tutorial #Networks
YouTube
Network Analysis Tutorial: Introduction to Networks
This is the first video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1
From online social networks such as Facebook and Twitter to transportation networks such as bike sharingโฆ
From online social networks such as Facebook and Twitter to transportation networks such as bike sharingโฆ
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๐ The Basics Of Social Network Analysis: A Social Network Lab in R for Beginners
๐ฅFree recorded Tutorial
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks #R #code
๐ฅFree recorded Tutorial
๐ฝ Watch
๐ฒChannel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks #R #code
YouTube
The Basics of Social Network Analysis: A Social Network Lab in R for Beginners
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ
So you want to get started with social network analysis but need a foundation or a refresher? This video covers exactly what we mean by a โnetworkโ and is theโฆ
So you want to get started with social network analysis but need a foundation or a refresher? This video covers exactly what we mean by a โnetworkโ and is theโฆ
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