📹Scale Free Complex Networks
💥Free recorded tutorial from Albert-László Barabási as the author of the best-seller book, Linked: The New Science of Networks.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#tutorial #video
💥Free recorded tutorial from Albert-László Barabási as the author of the best-seller book, Linked: The New Science of Networks.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#tutorial #video
YouTube
Scale Free Complex Networks
You might know Albert-László Barabási as the author of the best-seller book, Linked: The New Science of Networks. Professor Barabási's seminal work led to the understanding of the common structure of diverse complex systems: natural, technological, and social.…
🎞 Use of Python for Complex Network Analysis
💥Free recorded tutorial from Andre Voigt who is a PhD candidate in Eivind Almaas' group at NTNU
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Free recorded tutorial from Andre Voigt who is a PhD candidate in Eivind Almaas' group at NTNU
📽 Watch
📲Channel: @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 their…
Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their…
🎞 Network Analysis Made Simple | Scipy 2019 Tutorial | Eric Ma
💥Free recorded tutorial
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Free recorded tutorial
📽 Watch
📲Channel: @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 datasets…
📃Network analysis of protein interaction data: an introduction
💥Good introductory document from EBI
🌐 Study the tutorial
📲Channel: @ComplexNetworkAnalysis
#tutorial
💥Good introductory document from EBI
🌐 Study the tutorial
📲Channel: @ComplexNetworkAnalysis
#tutorial
🎞 Social Network Analysis
💥This free recorded tutorial is an overview of social networks and social network analysis.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥This free recorded tutorial is an overview of social networks and social network analysis.
📽Watch
📱Channel: @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/
🎞 Gephi Tutorial on Network Visualization and Analysis
💥This free recorded tutorial goes from import through the whole analysis phase for a citation network.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial #gephi
💥This free recorded tutorial goes from import through the whole analysis phase for a citation network.
📽 Watch
📱Channel: @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
🎞 Emergence of echo chambers and polarization dynamics in social networks
💥Echo 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.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Echo 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.
📽 Watch
📱Channel: @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 on…
Abstract: Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on…
🎞 Order and Disorder in Network Science
💥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 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.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥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 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.
📽 Watch
📱Channel: @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 microscopic…
📄Machine Learning in Network Centrality Measures: Tutorial and Outlook
📘Journal: ACM Computing Surveys (I.F=10.282)
🗓Publish year: 2019
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #tutorial #centrality
📘Journal: ACM Computing Surveys (I.F=10.282)
🗓Publish year: 2019
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #tutorial #centrality
🎞 Introduction to Data Science - NetworkX Tutorial
💥Free recorded tutorial.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Free recorded tutorial.
📽 Watch
📱Channel: @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
👍1
🎞 Social network analysis: Considerations for data collection and analysis
💥Free recorded tutorial.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Free recorded tutorial.
📽 Watch
📱Channel: @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 network…
📄A tutorial on modeling and analysis of dynamic social networks. Part I
📘Journal: Annual Reviews in Control (I.F=10.699)
🗓Publish year: 2017
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #tutorial #dynamic
📘Journal: Annual Reviews in Control (I.F=10.699)
🗓Publish year: 2017
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #tutorial #dynamic
🎞GEPHI – Introduction to Network Analysis and Visualization
💥Free online tutorial and recorded course
💥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 everyone to make his first experiments on two complementary datasets.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Gephi #tutorial
💥Free online tutorial and recorded course
💥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 everyone to make his first experiments on two complementary datasets.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Gephi #tutorial
www.martingrandjean.ch
GEPHI – 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 everyone…
👍1
🎞 Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
💥Free recorded tutorial by Avkash Chauhan.
💥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 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.
📽 Watch: part1 part2
💻 Code
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #tutorial #Graph #GNN #Python #NetworkX #PyG
💥Free recorded tutorial by Avkash Chauhan.
💥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 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.
📽 Watch: part1 part2
💻 Code
📜 Slides
📲Channel: @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 various…
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…