2020_Social_network_analysis_of_open_source_software_A_review.pdf
715.7 KB
πSocial network analysis of open source software: A review and categorisation
πJournal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
πJournal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
πUsing Theory to Guide Exploratory Network Analyses
πJournal: Faculty & Staff Research and Creative Activity
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
πJournal: Faculty & Staff Research and Creative Activity
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
πBlockchain Network Analysis: A Comparative Study of Decentralized Banks?
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Blockchain #Banks #review
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Blockchain #Banks #review
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Social Network Analysis.pdf
2 MB
πSocial Network Analysis
πAuthors: StΓ©phane TuffΓ©ry
π₯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.
π publish year: 2022
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #R #code
πAuthors: StΓ©phane TuffΓ©ry
π₯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.
π publish year: 2022
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #R #code
πSurvey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing
πJournal: Security and communication networks (IF= 1.288)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
πJournal: Security and communication networks (IF= 1.288)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
πGraph Learning: A Survey
πJournal: IEEE Transactions on Artificial Intelligence
πPublish year: 2021
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
πJournal: IEEE Transactions on Artificial Intelligence
πPublish year: 2021
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
2016-A Taxonomy and Survey of Dynamic Graph Visualization.pdf
3.2 MB
πA Taxonomy and Survey of Dynamic Graph Visualization
πJournal: Computer Graphics Forum (I.F= 1.6)
πPublish year: 2016
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey #Visualization
πJournal: Computer Graphics Forum (I.F= 1.6)
πPublish year: 2016
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey #Visualization
π1
πTime Series Forecasting Based on Complex Network Analysis
πJournal: IEEE Access (I.F= 4.809)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Forecasting #Time_Series
πJournal: IEEE Access (I.F= 4.809)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Forecasting #Time_Series
π1
2016-Complex network analysis of time series.pdf
948 KB
πComplex network analysis of time series
πJournal: EPL (I.F= 1.947)
πPublish year: 2016
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Time_Series
πJournal: EPL (I.F= 1.947)
πPublish year: 2016
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Time_Series
πWhat Is Graph Analytics?
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
Medium
What Is Graph Analytics?
Basic Introduction to what Graph Analytics isβ¦
πUsing social network analysis to examine alcohol use among adults: A systematic review
πJournal: PLOS ONE (I.F=3.752)
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #examine #alcohol #adults #review
πJournal: PLOS ONE (I.F=3.752)
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #examine #alcohol #adults #review
πGraph analysis to survey data: a ο¬rst approximation
πJournal: Complex Systems in Science (I.F=0.36)
πPublish year: 2015
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #survey
πJournal: Complex Systems in Science (I.F=0.36)
πPublish year: 2015
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #survey
π A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
π₯Free recorded tutorial by Andre M. Bastos
πΉThis 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
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Tutorial #Connectivity #review
π₯Free recorded tutorial by Andre M. Bastos
πΉThis 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
π½Watch
π±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 advantagesβ¦
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 advantagesβ¦
πSocial network analysis in operations and supply chain management: a review and revised research agenda
πJournal: INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT (I.F=9.36)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #supply #chain_management #agenda #review
πJournal: INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT (I.F=9.36)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #supply #chain_management #agenda #review
πWhat Is Graph Analytics & Its Top Tools
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
π₯Technical paper
π Study
π²Channel: @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
πA Network Analysis of Twitter Interactions by Members of the U.S. Congress
πJournal: ACM Transactions on Social Computing
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Twitter #Congress
πJournal: ACM Transactions on Social Computing
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Twitter #Congress
πRecommending on Graphs: A Comprehensive Review from Data Perspective
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
Forwarded from Bioinformatics
πGraph representation learning in bioinformatics: trends, methods and applications
πJournal: Briefings in Bioinformatics (I.F.=11.622)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #graph_representation_learning
πJournal: Briefings in Bioinformatics (I.F.=11.622)
πPublish year: 2022
π Study the paper
π²Channel: @Bioinformatics
#review #graph_representation_learning
π Co-expression network analysis using RNA-Seq data
π₯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 β College Park (June 15 2016).
πΉThis 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.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Co_expression_network #RNA_Seq
π₯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 β College Park (June 15 2016).
πΉThis 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.
π½Watch
π±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 provideβ¦
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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 provideβ¦
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