🎞 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
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📱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…
📄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
📎 Study the paper
📲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
🎞 Co-expression network analysis using RNA-Seq data
💥Free recorded tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland – College Park (June 15 2016).
🔹This tutorial provide a simple overview of co-expression network analysis, with an emphasis on the use of RNA-Seq data.A motivation for the use of co-expression network analysis is provided and compared to other common types of RNA-Seq analyses such as differential expression analysis and gene set enrichment analysis. The use of adjacency matrices to represent networks is explored for several different types of networks and a small synthetic dataset is used to demonstrate each of the major steps in co-expression network construction and module detection. The tutorial portion of the presentation then applies some of these principles using a real dataset containing ~3000 genes, after filtering.
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📱Channel: @ComplexNetworkAnalysis
#video #Co_expression_network #RNA_Seq
💥Free recorded tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland – College Park (June 15 2016).
🔹This tutorial provide a simple overview of co-expression network analysis, with an emphasis on the use of RNA-Seq data.A motivation for the use of co-expression network analysis is provided and compared to other common types of RNA-Seq analyses such as differential expression analysis and gene set enrichment analysis. The use of adjacency matrices to represent networks is explored for several different types of networks and a small synthetic dataset is used to demonstrate each of the major steps in co-expression network construction and module detection. The tutorial portion of the presentation then applies some of these principles using a real dataset containing ~3000 genes, after filtering.
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📱Channel: @ComplexNetworkAnalysis
#video #Co_expression_network #RNA_Seq
YouTube
DC ISCB Workshop 2016 - Co-expression network analysis using RNA-Seq data (Keith Hughitt)
Overview
---------------
Tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland - College Park (June 15 2016).
Abstract
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In this presentation, I provide…
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Tutorial on Co-expression network analysis using RNA-Seq data presented at the ISCB DC Regional Student Group Workshop at the University of Maryland - College Park (June 15 2016).
Abstract
--------------
In this presentation, I provide…
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📄COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
📘Journal: JOURNAL OF MEDICAL INTERNET RESEARCH (I.F=7.076)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #5G_Conspiracy #Twitter
📘Journal: JOURNAL OF MEDICAL INTERNET RESEARCH (I.F=7.076)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #5G_Conspiracy #Twitter
📄SBEToolbox: A Matlab Toolbox for Biological Network Analysis
📘Journal: Evolutionary Bioinformatics (I.F= 1.625)
🗓Publish year: 2012
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Matlab #tool #Biological_Network
📘Journal: Evolutionary Bioinformatics (I.F= 1.625)
🗓Publish year: 2012
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Matlab #tool #Biological_Network
2020_Social Network Analysis using Python Data Mining.pdf
829.6 KB
📄Social Network Analysis using Python Data Mining
📘Conference: International Conference on Cyber and IT Service Management (CITSM)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Python #Data_Mining
📘Conference: International Conference on Cyber and IT Service Management (CITSM)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Python #Data_Mining
🎞 Financial Network Analysis using Python
💥Free recorded tutorial on Financial Network Analysis using Python by Kalyan Prasad (Data Scientist & Analytics Manager at Creative Crewz).
🔹To model the stock market using network analysis, different stocks are represented as different nodes. However, defining the interaction, or creating edges, between different nodes is rather non-intuitive, unlike some physical networks, such as friendship network, in which interaction between different nodes can be defined explicitly. A traditional way to create edges between different nodes for stock market is to look at the correlations of some defined attributes. In this tutorial analyze one of the reputed stock index data and identifies stock relationships in it. and propose a model that can depict such relationships and create networks of stocks.and investigate and create different networks according to the degree of correlation of stocks.
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📱Channel: @ComplexNetworkAnalysis
#video #Financial #Python
💥Free recorded tutorial on Financial Network Analysis using Python by Kalyan Prasad (Data Scientist & Analytics Manager at Creative Crewz).
🔹To model the stock market using network analysis, different stocks are represented as different nodes. However, defining the interaction, or creating edges, between different nodes is rather non-intuitive, unlike some physical networks, such as friendship network, in which interaction between different nodes can be defined explicitly. A traditional way to create edges between different nodes for stock market is to look at the correlations of some defined attributes. In this tutorial analyze one of the reputed stock index data and identifies stock relationships in it. and propose a model that can depict such relationships and create networks of stocks.and investigate and create different networks according to the degree of correlation of stocks.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Financial #Python
YouTube
Financial Network Analysis using Python | Kalyan Prasad | Conf42 Python 2022
Kalyan Prasad
Data Scientist & Analytics Manager at Creative Crewz
Historically, networks have been studied extensively in graph theory, an area of mathematics. After many applications to several different subjects including physics, health science, and…
Data Scientist & Analytics Manager at Creative Crewz
Historically, networks have been studied extensively in graph theory, an area of mathematics. After many applications to several different subjects including physics, health science, and…
📹 Collect, process and visualize Instagram social network
💥In this video, we will collect social data from Instagram, process it using Python and then create a graph with Gephi,
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📲Channel: @ComplexNetworkAnalysis
#video #python #gephi #instagram
💥In this video, we will collect social data from Instagram, process it using Python and then create a graph with Gephi,
🌐 Watch
📲Channel: @ComplexNetworkAnalysis
#video #python #gephi #instagram
YouTube
Collect, Process, Visualize - Programming Social Graphs (Instagram, Python, Gephi)
Introduction to network analysis on a real-world example. In this video, we will collect social data from Instagram, process it using Python and then create a graph with Gephi, an open-source graph visualization and exploration tool. I will demonstrate the…
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📄Picture This: How Graph Analytics Simplifies Complex Insights
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
CIO
Picture This: How Graph Analytics Simplifies Complex Insights
With the advancement of computational platforms and corresponding software, enterprises have huge opportunities to leverage graph technology to create competitive advantages over their peers.
📄Studying Fake News via Network Analysis: Detection and Mitigation
📘In book: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (pp.43-65)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fake_News #Detection #Mitigation
📘In book: Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (pp.43-65)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fake_News #Detection #Mitigation
🎞 Getting Started with Network Data Using Gephi
💥Free recorded workshop from UCR Library
🔹In this workshop, you'll become familiar with essential vocabulary and concepts related to network graphs and network analysis while gaining experience with Gephi's interface and tools for analyzing and visualizing networks.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Gephi
💥Free recorded workshop from UCR Library
🔹In this workshop, you'll become familiar with essential vocabulary and concepts related to network graphs and network analysis while gaining experience with Gephi's interface and tools for analyzing and visualizing networks.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Gephi
YouTube
Getting Started with Network Data Using Gephi
In this workshop, you'll become familiar with essential vocabulary and concepts related to network graphs and network analysis while gaining experience with Gephi's interface and tools for analyzing and visualizing networks.
👍1
📄Graph neural networks: A review of methods and applications
📘Journal: AI Open(I.F= 14.05)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #review
📘Journal: AI Open(I.F= 14.05)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #review
👍1
📄A Gentle Introduction to Graph Neural Network
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN
Introduction to Neural Networks Using PyTorch.pdf
308.2 KB
📕Introduction to Neural Networks Using PyTorch
📝Authors: Pradeepta Mishra
💥Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because they were invented in 1950s.
🗓 publish year: 2022
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #Python #code
📝Authors: Pradeepta Mishra
💥Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because they were invented in 1950s.
🗓 publish year: 2022
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #Python #code
👍2
2021_A_systematic_review_of_network_analysis_studies_in_eating_disorders.pdf
533.5 KB
📄A systematic review of network analysis studies in eating disorders: Is time to broaden the core psychopathology to non specific symptoms
📘Journal: EUROPEAN EATING DISORDERS REVIEW (I.F=5.36)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #eating_disorders #broaden #psychopathology #symptoms #review
📘Journal: EUROPEAN EATING DISORDERS REVIEW (I.F=5.36)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #eating_disorders #broaden #psychopathology #symptoms #review
📄Reversibility of link prediction and its application to epidemic mitigation
📘Journal: scientific reports (I.F= 4.379)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction
📘Journal: scientific reports (I.F= 4.379)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction