2019_Complex_networks_analysis_in_Iran_stock_market_The_application.pdf
886.4 KB
πComplex networks analysis in Iran stock market: The application of centrality
πJournal: Physica A: Statistical Mechanics and its Applications (I.F= 3.263)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #centrality
πJournal: Physica A: Statistical Mechanics and its Applications (I.F= 3.263)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #centrality
π3
2019_Complex_network_analysis_of_keywords_co_occurrence_in_the_recent.pdf
3.2 MB
πComplex network analysis of keywords co-occurrence in the recent efficiency analysis literature
πJournal: Scientometrics (I.F= 3.238)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Co_occurrence_network
πJournal: Scientometrics (I.F= 3.238)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Co_occurrence_network
π1
πThe network approach to psychopathology: a review of the literature 2008β2018 and an agenda for future research
πJournal: PSYCHOLOGICAL MEDICINE (I.F=10.592)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #psychopathology #review
πJournal: PSYCHOLOGICAL MEDICINE (I.F=10.592)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #psychopathology #review
2018_Critical_Review_of_Social_Network_Analysis_Applications_in.pdf
6.1 MB
πCritical Review of Social Network Analysis Applications in Complex Project Management
πJournal: JOURNAL OF MANAGEMENT IN ENGINEERING (I.F=6.415)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #Project #Management #review
πJournal: JOURNAL OF MANAGEMENT IN ENGINEERING (I.F=6.415)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #Project #Management #review
π How to Analyze Social Media Networks with Kumu and NodeXL
π₯Free recorded tutorial
πΉNodeXL 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
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Kumu #NodeXL
π₯Free recorded tutorial
πΉNodeXL 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
π½ Watch
π±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)β¦
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)β¦
π2
2016_A Complex Network Perspective on Clinical Science.pdf
563.8 KB
πA Complex Network Perspective on Clinical Science
πJournal: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE (I.F=11.621)
πPublish year: 2016
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Clinical_Science
πJournal: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE (I.F=11.621)
πPublish year: 2016
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Clinical_Science
πGraph neural networks for affective social media: A comprehensive overview
πConference: THECOG
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #social_media #overview
πConference: THECOG
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #social_media #overview
πBooklet: Structure and Dynamics of Information in Networks
π₯David Kempe, Department of Computer Science, University of Southern California
πPublish year: 2021
πStudy Booklet
π±Channel: @ComplexNetworkAnalysis
#Booklet #Structure #Dynamics #Information
π₯David Kempe, Department of Computer Science, University of Southern California
πPublish year: 2021
πStudy Booklet
π±Channel: @ComplexNetworkAnalysis
#Booklet #Structure #Dynamics #Information
π3
2022_Basics_on_network_theory_to_analyze_biological_systems_a_hands.pdf
3.9 MB
πBasics on network theory to analyze biological systems: a hands-on outlook
πJournal: Functional & Integrative Genomics (I.F= 3.41)
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #biological_network
πJournal: Functional & Integrative Genomics (I.F= 3.41)
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #biological_network
2021_Graph_Theoretical_Analysis_of_Brain_Network_Characteristics.pdf
1.4 MB
πGraph Theoretical Analysis of Brain Network Characteristics in Brain Tumor Patients: A Systematic Review
πJournal: Neuropsychology Review (I.F= 6.75)
πPublish year: 2021
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Brain #Review
πJournal: Neuropsychology Review (I.F= 6.75)
πPublish year: 2021
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Brain #Review
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¦