π Eric Ma: Network Analysis Made Simple
π₯Free recorded tutorial
πΉHave you ever wondered about how data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, visualizing, and using complex networks to solve problems
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video
π₯Free recorded tutorial
πΉHave you ever wondered about how data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, visualizing, and using complex networks to solve problems
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video
YouTube
Tutorial - Eric Ma: Network Analysis Made Simple
Have you ever wondered about how data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasetsβ¦
π Machine Learning with Graphs: PageRank Random Walks and embedding
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture they focus on how to represent graphs as matrices and discuss subsequent properties that can explore. then define the notion of PageRank, further explore Random Walks, and introduce Matrix Factorization as a perspective for generating node embeddings.
π½ Watch: part1 part2 part3 part4
π Slides
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture they focus on how to represent graphs as matrices and discuss subsequent properties that can explore. then define the notion of PageRank, further explore Random Walks, and introduce Matrix Factorization as a perspective for generating node embeddings.
π½ Watch: part1 part2 part3 part4
π Slides
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 - PageRank
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pGwEKo
Jure Leskovec
Computer Science, PhD
In this lecture we focus on how to represent graphs as matrices and discuss subsequentβ¦
Jure Leskovec
Computer Science, PhD
In this lecture we focus on how to represent graphs as matrices and discuss subsequentβ¦
πA Review of Complex Systems Approaches to Cancer Networks
πJournal: Complex Systems
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #cancer #review
πJournal: Complex Systems
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #cancer #review
π3
πApplication of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects
πJournal: SUSTAINABILITY(I.F=3.889)
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Application #Causality #Defects
πJournal: SUSTAINABILITY(I.F=3.889)
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Application #Causality #Defects
πComplex Networks: ErdΕsβRΓ©nyi Model, Centralities, Random Regular Graph
π₯Complex Networks are traditionally studied in the context of Graph theory, and identify important nodes and edges with the notions of centrality.
π₯free online site to visualize, test and see different metrics in complex network.
π Link
π²Channel: @ComplexNetworkAnalysis
#paper #Centralities
π₯Complex Networks are traditionally studied in the context of Graph theory, and identify important nodes and edges with the notions of centrality.
π₯free online site to visualize, test and see different metrics in complex network.
π Link
π²Channel: @ComplexNetworkAnalysis
#paper #Centralities
π Conducting Network Analysis in R
π₯Free recorded webinar
πΉThis webinar, which is sponsored by the AED Early Career Special Interest Group (SIG), will provide guidance on how network analysis is a statistical approach that allows for the examination of how components of a network are related to one another.In this webinar, Dr. Cheri Levinson and her advanced graduate student Ms. Irina Vanzhula will provide a brief overview on network theory and analysis. They will then demonstrate how to conduct network analysis in R using sample data.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #R
π₯Free recorded webinar
πΉThis webinar, which is sponsored by the AED Early Career Special Interest Group (SIG), will provide guidance on how network analysis is a statistical approach that allows for the examination of how components of a network are related to one another.In this webinar, Dr. Cheri Levinson and her advanced graduate student Ms. Irina Vanzhula will provide a brief overview on network theory and analysis. They will then demonstrate how to conduct network analysis in R using sample data.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #R
YouTube
Conducting Network Analysis in R
Conducting Network Analysis in R
March 26, 2020 at 2:00 PM (EST)
Speakers: Cheri Levinson and Irina Vanzhula
Moderator: Kathryn Coniglio
This webinar, which is sponsored by the AED Early Career Special Interest Group (SIG), will provide guidance on how networkβ¦
March 26, 2020 at 2:00 PM (EST)
Speakers: Cheri Levinson and Irina Vanzhula
Moderator: Kathryn Coniglio
This webinar, which is sponsored by the AED Early Career Special Interest Group (SIG), will provide guidance on how networkβ¦
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