🎞 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…
📕Introduction to R for Data Science: A LISA 2020 Guidebook
📝Authors: Jacob D. Holster
💥This guidebook aims to provide readers an opportunity to make a start towards learning R for a variety of data science tasks, include (a) data cleaning and preparation, (b) statistical analysis, (c) data visualization, (d) natural language processing, (e) network analysis, and (f) Structural Equation Modeling to name a few. In Chapters 1 and 2 we invite readers to install R and RStudio and to start manipulating data for analysis. Chapter 3 and Chapter 4 include introductory exercises to teach data visualization and statistical analysis in R. In Chapter 5 and beyond, you will explore basic analytic concepts (e.g., correlation and regression) and more advanced approaches to data modeling through the lenses of Structural Equation Modeling, Network Analysis, and Text Analysis.
📚Free online guidebook
📖 Study
💻 Code
📲Channel: @ComplexNetworkAnalysis
#book #R #code #video
📝Authors: Jacob D. Holster
💥This guidebook aims to provide readers an opportunity to make a start towards learning R for a variety of data science tasks, include (a) data cleaning and preparation, (b) statistical analysis, (c) data visualization, (d) natural language processing, (e) network analysis, and (f) Structural Equation Modeling to name a few. In Chapters 1 and 2 we invite readers to install R and RStudio and to start manipulating data for analysis. Chapter 3 and Chapter 4 include introductory exercises to teach data visualization and statistical analysis in R. In Chapter 5 and beyond, you will explore basic analytic concepts (e.g., correlation and regression) and more advanced approaches to data modeling through the lenses of Structural Equation Modeling, Network Analysis, and Text Analysis.
📚Free online guidebook
📖 Study
💻 Code
📲Channel: @ComplexNetworkAnalysis
#book #R #code #video
2020_Social_network_analysis_of_open_source_software_A_review.pdf
715.7 KB
📄Social network analysis of open source software: A review and categorisation
📘Journal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
📘Journal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
📄Using Theory to Guide Exploratory Network Analyses
📘Journal: Faculty & Staff Research and Creative Activity
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📘Journal: Faculty & Staff Research and Creative Activity
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📄Blockchain Network Analysis: A Comparative Study of Decentralized Banks?
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Blockchain #Banks #review
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Blockchain #Banks #review
👍1
Social Network Analysis.pdf
2 MB
📕Social Network Analysis
📝Authors: Stéphane Tufféry
💥Social networks are at the heart of big data, with their huge quantities of data of all kinds, text, images, video, and audio. Graphs are used to represent social networks in particular and all networks in general. In many applications of social networks, it is important to identify the most influential individuals. In a graph, the importance of a vertex can be expressed in several ways, the main ones being the degree centrality, the closeness centrality, the betweenness centrality, and prestige. A clique is a graph in which all vertices are connected and a quasi-clique is a group of vertices that are highly connected. A community is a subgraph that is both a quasi-clique and a quasi-connected component.
🗓 publish year: 2022
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #R #code
📝Authors: Stéphane Tufféry
💥Social networks are at the heart of big data, with their huge quantities of data of all kinds, text, images, video, and audio. Graphs are used to represent social networks in particular and all networks in general. In many applications of social networks, it is important to identify the most influential individuals. In a graph, the importance of a vertex can be expressed in several ways, the main ones being the degree centrality, the closeness centrality, the betweenness centrality, and prestige. A clique is a graph in which all vertices are connected and a quasi-clique is a group of vertices that are highly connected. A community is a subgraph that is both a quasi-clique and a quasi-connected component.
🗓 publish year: 2022
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #R #code
📄Survey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing
📘Journal: Security and communication networks (IF= 1.288)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
📘Journal: Security and communication networks (IF= 1.288)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
📄Graph Learning: A Survey
📘Journal: IEEE Transactions on Artificial Intelligence
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
📘Journal: IEEE Transactions on Artificial Intelligence
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
2016-A Taxonomy and Survey of Dynamic Graph Visualization.pdf
3.2 MB
📄A Taxonomy and Survey of Dynamic Graph Visualization
📘Journal: Computer Graphics Forum (I.F= 1.6)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey #Visualization
📘Journal: Computer Graphics Forum (I.F= 1.6)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey #Visualization
👍1
📄Time Series Forecasting Based on Complex Network Analysis
📘Journal: IEEE Access (I.F= 4.809)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Forecasting #Time_Series
📘Journal: IEEE Access (I.F= 4.809)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Forecasting #Time_Series
👍1
2016-Complex network analysis of time series.pdf
948 KB
📄Complex network analysis of time series
📘Journal: EPL (I.F= 1.947)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Time_Series
📘Journal: EPL (I.F= 1.947)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Time_Series
📄Using social network analysis to examine alcohol use among adults: A systematic review
📘Journal: PLOS ONE (I.F=3.752)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #examine #alcohol #adults #review
📘Journal: PLOS ONE (I.F=3.752)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #examine #alcohol #adults #review
📄Graph analysis to survey data: a first approximation
📘Journal: Complex Systems in Science (I.F=0.36)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #survey
📘Journal: Complex Systems in Science (I.F=0.36)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #survey
🎞 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-noscripts, 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-noscripts, 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
Denoscription: 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…
Denoscription: 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…
📄Social network analysis in operations and supply chain management: a review and revised research agenda
📘Journal: INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT (I.F=9.36)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #supply #chain_management #agenda #review
📘Journal: INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT (I.F=9.36)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #supply #chain_management #agenda #review
📄What Is Graph Analytics & Its Top Tools
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
Analytics India Magazine
What Is Graph Analytics & Its Top Tools
Graph analytics are analytic tools that are used to analyze relations and determine strength between the entities.
2021_A_Network_Analysis_of_Twitter_Interactions_by_Members_of_the.pdf
2.9 MB
📄A Network Analysis of Twitter Interactions by Members of the U.S. Congress
📘Journal: ACM Transactions on Social Computing
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Twitter #Congress
📘Journal: ACM Transactions on Social Computing
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Twitter #Congress
📄Recommending on Graphs: A Comprehensive Review from Data Perspective
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
Forwarded from Bioinformatics
📃Graph representation learning in bioinformatics: trends, methods and applications
📘Journal: Briefings in Bioinformatics (I.F.=11.622)
🗓Publish year: 2022
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📲Channel: @Bioinformatics
#review #graph_representation_learning
📘Journal: Briefings in Bioinformatics (I.F.=11.622)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @Bioinformatics
#review #graph_representation_learning