πImplementation and Understanding of Graph Neural Networks(GNN)
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #code #PyTorch
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #code #PyTorch
Medium
Implementation and Understanding of Graph Neural Networks(GNN)
Neural Networks are good at capturing hidden patterns of Euclidean data (images, text, videos). But what about applications where data isβ¦
π£ Graph Structure and Complex Network Analysis
π₯INTERNATIONAL CENTER FOR PURE AND ACCURATE MATHEMATICS
π₯Understanding the graph structure is a key point in deriving efficient algorithms in large networks. In this school, we will cover theoretical aspects of graph structure analysis as well as applications on complex network studies with 9 lectures in two main axes:
1) Exploiting graph structure to efficiently solve combinatorial problems
2) Extending graph structural analysis to complex network studies
π SIRINCE , Turkey
π¬ Language: English
π 04/06/2023 to 16/06/2023
π Deadline : February 21, 2023
π¨βπ« Scientific committee:
TΔ±naz EkiΜm, Bertrand Jouve, Pascale KUNTZ, Saieed Akbari, PΔ±nar Heggernes, Marc Demange
πLink
βΉοΈ Register + more information
π²Channel: @ComplexNetworkAnalysis
#CIMPA_schools
π₯INTERNATIONAL CENTER FOR PURE AND ACCURATE MATHEMATICS
π₯Understanding the graph structure is a key point in deriving efficient algorithms in large networks. In this school, we will cover theoretical aspects of graph structure analysis as well as applications on complex network studies with 9 lectures in two main axes:
1) Exploiting graph structure to efficiently solve combinatorial problems
2) Extending graph structural analysis to complex network studies
π SIRINCE , Turkey
π¬ Language: English
π 04/06/2023 to 16/06/2023
π Deadline : February 21, 2023
π¨βπ« Scientific committee:
TΔ±naz EkiΜm, Bertrand Jouve, Pascale KUNTZ, Saieed Akbari, PΔ±nar Heggernes, Marc Demange
πLink
βΉοΈ Register + more information
π²Channel: @ComplexNetworkAnalysis
#CIMPA_schools
π1
π Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
π₯Free recorded tutorial by Avkash Chauhan.
π₯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.
π½ Watch: part1 part2
π» Code
π Slides
π²Channel: @ComplexNetworkAnalysis
#video #tutorial #Graph #GNN #Python #NetworkX #PyG
π₯Free recorded tutorial by Avkash Chauhan.
π₯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.
π½ Watch: part1 part2
π» Code
π Slides
π²Channel: @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 variousβ¦
Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving variousβ¦
πIntroduction to R for Data Science: A LISA 2020 Guidebook
πAuthors: Jacob D. Holster
π₯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.
πFree online guidebook
π Study
π» Code
π²Channel: @ComplexNetworkAnalysis
#book #R #code #video
πAuthors: Jacob D. Holster
π₯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.
πFree online guidebook
π Study
π» Code
π²Channel: @ComplexNetworkAnalysis
#book #R #code #video
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
π1
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