📄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
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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
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📄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
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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
📎 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.
📽Watch
📱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.
📽Watch
📱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