📃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.
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📱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
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🎞 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…
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🎞 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…
🎞 A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
💥Free recorded tutorial by Andre M. Bastos
🔹This 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
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Tutorial #Connectivity #review
💥Free recorded tutorial by Andre M. Bastos
🔹This 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
📽Watch
📱Channel: @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 advantages…
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 advantages…
🎞 Network Analysis Tutorial: Introduction to Networks
💥Free recorded Tutorial by Eric Ma
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #Networks
💥Free recorded Tutorial by Eric Ma
📽 Watch
📲Channel: @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 sharing…
From online social networks such as Facebook and Twitter to transportation networks such as bike sharing…
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🎞 The Basics Of Social Network Analysis: A Social Network Lab in R for Beginners
💥Free recorded Tutorial
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks #R #code
💥Free recorded Tutorial
📽 Watch
📲Channel: @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 “network” and is the…
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 “network” and is the…
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🎞 Course "Social Network Analysis" (Leonid Zhukov). Descriptive Network Analysis
💥Free recorded Tutorial by Professor Leonid Zhukov, School of Data Analysis and Artificial Intelligence
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks
💥Free recorded Tutorial by Professor Leonid Zhukov, School of Data Analysis and Artificial Intelligence
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks
YouTube
Course "Social Network Analysis" (Leonid Zhukov). Lecture 2. Descriptive Network Analysis
Course outline:
- Introduction to network science
- Descriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
- Introduction to network science
- Descriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
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🎞 Perform social network analysis in R Tutorial
💥Free recorded Tutorial by Professor Leonid Zhukov, School of Data Analysis and Artificial Intelligence
📽 Watch
💻 Data
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks #R #code
💥Free recorded Tutorial by Professor Leonid Zhukov, School of Data Analysis and Artificial Intelligence
📽 Watch
💻 Data
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #social_networks #R #code
YouTube
Perform social network analysis in R Tutorial
This tutorial outlines how to perform social network analysis in r. Social network analysis is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes and the ties…
🎞 Overview of Complex Networks
💥Free recorded Tutorial on overview of complex networks
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📱Channel: @ComplexNetworkAnalysis
#video #Tutorial #Overview
💥Free recorded Tutorial on overview of complex networks
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Tutorial #Overview
YouTube
Overview of Complex Networks
Episode 10, Principles of Complex Systems, Spring 2013, University of Vermont.
Overview of Complex Networks.
Overview of Complex Networks.
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📄Gephi Tutorial: How to use it for Network Analysis?
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
👍3
🎞Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
💥Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
💥Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
💥Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
💥Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
YouTube
Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
Organizers: Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
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📃Understanding Graph Databases: A Comprehensive Tutorial and Survey
🗓 Publish year: 2024
🧑💻Authors: Sydney Anuyah, Emmanuel Bolade, Oluwatosin Agbaakin
🏢Universities: Indiana University, Indianapolis, IN, USA
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Database #Tutorial #survey
🗓 Publish year: 2024
🧑💻Authors: Sydney Anuyah, Emmanuel Bolade, Oluwatosin Agbaakin
🏢Universities: Indiana University, Indianapolis, IN, USA
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Database #Tutorial #survey
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