🎞 How to Analyze Social Media Networks with Kumu and NodeXL
💥Free recorded tutorial
🔹NodeXL and Kumu are two powerful free tools for social network analysis. NodeXL is excellent for gathering social media data but it is more challenging (for beginners) to generate understandable visualizations. Kumu on the other hand is an excellent tool for social network analysis, but you can't collect social media data using the tool. Here's how to combine the two together
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Kumu #NodeXL
💥Free recorded tutorial
🔹NodeXL and Kumu are two powerful free tools for social network analysis. NodeXL is excellent for gathering social media data but it is more challenging (for beginners) to generate understandable visualizations. Kumu on the other hand is an excellent tool for social network analysis, but you can't collect social media data using the tool. Here's how to combine the two together
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Kumu #NodeXL
YouTube
Tutorial - How to Analyze Social Media Networks with Kumu and NodeXL
Update: Since X, or Twitter shut down the public api - this is probably no longer valid.
NodeXL and Kumu are two powerful free tools for social network analysis. NodeXL is excellent for gathering social media data but it is more challenging (for beginners)…
NodeXL and Kumu are two powerful free tools for social network analysis. NodeXL is excellent for gathering social media data but it is more challenging (for beginners)…
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2016_A Complex Network Perspective on Clinical Science.pdf
563.8 KB
📄A Complex Network Perspective on Clinical Science
📘Journal: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE (I.F=11.621)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Clinical_Science
📘Journal: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE (I.F=11.621)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Clinical_Science
📄Graph neural networks for affective social media: A comprehensive overview
📘Conference: THECOG
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #social_media #overview
📘Conference: THECOG
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #social_media #overview
📝Booklet: Structure and Dynamics of Information in Networks
💥David Kempe, Department of Computer Science, University of Southern California
🗓Publish year: 2021
📎Study Booklet
📱Channel: @ComplexNetworkAnalysis
#Booklet #Structure #Dynamics #Information
💥David Kempe, Department of Computer Science, University of Southern California
🗓Publish year: 2021
📎Study Booklet
📱Channel: @ComplexNetworkAnalysis
#Booklet #Structure #Dynamics #Information
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2022_Basics_on_network_theory_to_analyze_biological_systems_a_hands.pdf
3.9 MB
📄Basics on network theory to analyze biological systems: a hands-on outlook
📘Journal: Functional & Integrative Genomics (I.F= 3.41)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #biological_network
📘Journal: Functional & Integrative Genomics (I.F= 3.41)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #biological_network
2021_Graph_Theoretical_Analysis_of_Brain_Network_Characteristics.pdf
1.4 MB
📄Graph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review
📘Journal: Neuropsychology Review (I.F= 6.75)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Brain #Review
📘Journal: Neuropsychology Review (I.F= 6.75)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Brain #Review
🎞 Eric Ma: Network Analysis Made Simple
💥Free recorded tutorial
🔹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, 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
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video
💥Free recorded tutorial
🔹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, 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
📽 Watch
📱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 datasets…
🎞 Machine Learning with Graphs: PageRank Random Walks and embedding
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
📽 Watch: part1 part2 part3 part4
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
📽 Watch: part1 part2 part3 part4
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 - PageRank
For more information about Stanford’s 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 subsequent…
Jure Leskovec
Computer Science, PhD
In this lecture we focus on how to represent graphs as matrices and discuss subsequent…
📄A Review of Complex Systems Approaches to Cancer Networks
📘Journal: Complex Systems
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #cancer #review
📘Journal: Complex Systems
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #cancer #review
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📄Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects
📘Journal: SUSTAINABILITY(I.F=3.889)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Application #Causality #Defects
📘Journal: SUSTAINABILITY(I.F=3.889)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Application #Causality #Defects
📄Complex Networks: Erdős–Rényi Model, Centralities, Random Regular Graph
💥Complex Networks are traditionally studied in the context of Graph theory, and identify important nodes and edges with the notions of centrality.
💥free online site to visualize, test and see different metrics in complex network.
📎 Link
📲Channel: @ComplexNetworkAnalysis
#paper #Centralities
💥Complex Networks are traditionally studied in the context of Graph theory, and identify important nodes and edges with the notions of centrality.
💥free online site to visualize, test and see different metrics in complex network.
📎 Link
📲Channel: @ComplexNetworkAnalysis
#paper #Centralities
🎞 Conducting Network Analysis in R
💥Free recorded webinar
🔹This 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.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #R
💥Free recorded webinar
🔹This 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.
📽 Watch
📱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 network…
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 network…
📄Implementation and Understanding of Graph Neural Networks(GNN)
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #code #PyTorch
💥Technical paper
🌐 Study
📲Channel: @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 is…
📣 Graph Structure and Complex Network Analysis
💥INTERNATIONAL CENTER FOR PURE AND ACCURATE MATHEMATICS
💥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
📌 SIRINCE , Turkey
💬 Language: English
🗓 04/06/2023 to 16/06/2023
🕖 Deadline : February 21, 2023
👨🏫 Scientific committee:
Tınaz Eki̇m, Bertrand Jouve, Pascale KUNTZ, Saieed Akbari, Pınar Heggernes, Marc Demange
📎Link
ℹ️ Register + more information
📲Channel: @ComplexNetworkAnalysis
#CIMPA_schools
💥INTERNATIONAL CENTER FOR PURE AND ACCURATE MATHEMATICS
💥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
📌 SIRINCE , Turkey
💬 Language: English
🗓 04/06/2023 to 16/06/2023
🕖 Deadline : February 21, 2023
👨🏫 Scientific committee:
Tınaz Eki̇m, Bertrand Jouve, Pascale KUNTZ, Saieed Akbari, Pınar Heggernes, Marc Demange
📎Link
ℹ️ Register + more information
📲Channel: @ComplexNetworkAnalysis
#CIMPA_schools
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🎞 Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
💥Free recorded tutorial by Avkash Chauhan.
💥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.
📽 Watch: part1 part2
💻 Code
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #tutorial #Graph #GNN #Python #NetworkX #PyG
💥Free recorded tutorial by Avkash Chauhan.
💥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.
📽 Watch: part1 part2
💻 Code
📜 Slides
📲Channel: @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 various…
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…
📕Introduction to R for Data Science: A LISA 2020 Guidebook
📝Authors: Jacob D. Holster
💥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.
📚Free online guidebook
📖 Study
💻 Code
📲Channel: @ComplexNetworkAnalysis
#book #R #code #video
📝Authors: Jacob D. Holster
💥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.
📚Free online guidebook
📖 Study
💻 Code
📲Channel: @ComplexNetworkAnalysis
#book #R #code #video
2020_Social_network_analysis_of_open_source_software_A_review.pdf
715.7 KB
📄Social network analysis of open source software: A review and categorisation
📘Journal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
📘Journal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
📄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