2020_Statistical Network Analysis A Review.pdf
875.3 KB
πStatistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic
πJournal: INTERNATIONAL STATISTICAL REVIEW (I.F=1.946)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Application #Coronavirus #review
πJournal: INTERNATIONAL STATISTICAL REVIEW (I.F=1.946)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Application #Coronavirus #review
πApplied Social Network Analysis in Python by University of Michigan on Coursera
π₯Free course by Daniel Romero
π»Codes
π²Channel: @ComplexNetworkAnalysis
#code #course #Social_Network #python
π₯Free course by Daniel Romero
π»Codes
π²Channel: @ComplexNetworkAnalysis
#code #course #Social_Network #python
GitHub
GitHub - sambhipiyuushh/Applied-Social-Network-Analysis-in-Python-University-of-Michigan: Applied Social Network Analysis in Pythonβ¦
Applied Social Network Analysis in Python by University of Michigan on Coursera - sambhipiyuushh/Applied-Social-Network-Analysis-in-Python-University-of-Michigan
2021_A_survey_on_graph_based_methods_for_similarity_searches_in.pdf
1.6 MB
πA survey on graph-based methods for similarity searches in metric spaces
πJournal: Information Systems(I.F=7.453)
πPublish year: 2021
π²Channel: @ComplexNetworkAnalysis
#paper #survey #graph
πJournal: Information Systems(I.F=7.453)
πPublish year: 2021
π²Channel: @ComplexNetworkAnalysis
#paper #survey #graph
π Getting Started with Network Analytics in Python
π₯Free recorded tutorial by Eric Sims
πΉThis tutorial is about DataTalks.Club (the place to talk about data)
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Python
π₯Free recorded tutorial by Eric Sims
πΉThis tutorial is about DataTalks.Club (the place to talk about data)
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Python
YouTube
Getting Started with Network Analytics in Python - Eric Sims
Links:
- Code: https://github.com/EricPostMaster/getting-started-with-network-analysis-in-python
- Eric's LinkedIn: https://www.linkedin.com/in/ericsims2/
- Graph data: https://snap.stanford.edu/data/index.html
- 100 days of networks: https://www.linkeβ¦
- Code: https://github.com/EricPostMaster/getting-started-with-network-analysis-in-python
- Eric's LinkedIn: https://www.linkedin.com/in/ericsims2/
- Graph data: https://snap.stanford.edu/data/index.html
- 100 days of networks: https://www.linkeβ¦
Social_Network_Theory_in_Construction_Industry_A_Scientometric_Review.pdf
414.4 KB
πSocial Network Theory in Construction Industry: A Scientometric Review
πConference: Recent Trends in Civil Engineering
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Review #Social_Network
πConference: Recent Trends in Civil Engineering
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Review #Social_Network
On Anomaly Detection in Graphs as Node Classification.pdf
467.2 KB
πOn Anomaly Detection in Graphs as Node Classification
πConference: Big Data Management and Analysis for Cyber Physical Systems
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #graph
πConference: Big Data Management and Analysis for Cyber Physical Systems
πPublish year: 2022
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #graph
πGraph Learning Approaches to Recommender Systems: A Review
πJournal: Information Retrieval
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommender_Systems #Graph #review
πJournal: Information Retrieval
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Recommender_Systems #Graph #review
π1
π Graph Theory: Nearest Neighbor Algorithm (NNA)
π₯Free recorded tutorial
πΉThis tutorial is about Nearest neighbour algorithm, Travelling salesman problem, Heuristic, Hamiltonian path
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph
π₯Free recorded tutorial
πΉThis tutorial is about Nearest neighbour algorithm, Travelling salesman problem, Heuristic, Hamiltonian path
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph
YouTube
Graph Theory: Nearest Neighbor Algorithm (NNA)
This lesson explains how to apply the nearest neightbor algorithm to try to find the lowest cost Hamiltonian circuit.
Site: https://mathispower4u.com
Site: https://mathispower4u.com
π1
πAn Introduction to Graph Theory
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
Built In
An Introduction to Graph Theory
Graph Theory is the study of relationships using vertices connected by edges. It is a helpful tool to quantify and simplify complex systems.
πA Note on Graph-Based Nearest Neighbor Search
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph
π4
πNetwork Analysis for the Digital Humanities: Principles, Problems, Extensions
πJournal: ISIS
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Digital #Humanities #Principles #Problems #Extensions
πJournal: ISIS
πPublish year: 2019
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Digital #Humanities #Principles #Problems #Extensions
πNetwork analysis on political election; populist vs social emergent behaviour
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
2022_Knowledge_Graphs_A_Practical_Review_of_the_Research_Landscape.pdf
510 KB
πKnowledge Graphs: A Practical Review of the Research Landscape
πJournal: INFORMATION
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
πJournal: INFORMATION
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
πKnowledge Graph Completion: A Birdβs Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
π Machine Learning with Graphs: PageRank Random Walks and embedding
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture, -we will talk about an alternative approach, message passing. We will introduce the semi-supervised learning on predicting node labels by leveraging correlations that exist in the network. One key concept is the collective classification, which involves three steps including the local classifier that assigns initial labels, the relational classifier that captures correlations, and the collective inference that propagates correlations.
-we introduce belief propagation, which is a dynamic programming approach to answering probability queries in a graph. By iteratively passing messages to neighbors, the final belief is calculated if a consensus is reached. We then show the message passing with examples and generalization to tree structure. At last, we talk about the loopy belief propagation algorithm, and its pros and cons.
-we introduce the relational classifier and iterative classification for node classification. Starting from the relational classifier, we show how to iteratively update probabilities of node labels based on the labels of neighbors. We then talk about the iterative classification that improves the collective classification by predicting node label based on labels of neighbors as well as its features
π½ Watch: part1 part2 part3
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯In this lecture, -we will talk about an alternative approach, message passing. We will introduce the semi-supervised learning on predicting node labels by leveraging correlations that exist in the network. One key concept is the collective classification, which involves three steps including the local classifier that assigns initial labels, the relational classifier that captures correlations, and the collective inference that propagates correlations.
-we introduce belief propagation, which is a dynamic programming approach to answering probability queries in a graph. By iteratively passing messages to neighbors, the final belief is calculated if a consensus is reached. We then show the message passing with examples and generalization to tree structure. At last, we talk about the loopy belief propagation algorithm, and its pros and cons.
-we introduce the relational classifier and iterative classification for node classification. Starting from the relational classifier, we show how to iteratively update probabilities of node labels based on the labels of neighbors. We then talk about the iterative classification that improves the collective classification by predicting node label based on labels of neighbors as well as its features
π½ Watch: part1 part2 part3
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 5.1 - Message passing and Node Classification
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jHRiGj
Jure Leskovec
Computer Science, PhD
From previous lectures, we learn the use of graph representation learning for node classification.β¦
Jure Leskovec
Computer Science, PhD
From previous lectures, we learn the use of graph representation learning for node classification.β¦
πTaxonomy of Link Prediction for Social Network Analysis: A Review
πJournal: IEEE Access (I.F=3.476)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
πJournal: IEEE Access (I.F=3.476)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
πKnowledge graph and knowledge reasoning: A systematic review
πJournal: Journal of Electronic Science and Technology
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
πJournal: Journal of Electronic Science and Technology
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
970.4 KB
πKnowledge Graph Embedding: A Survey of Approaches and Applications
πJournal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
πPublish year: 2017
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
πJournal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
πPublish year: 2017
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
πGamification in education: A citation network analysis using
CitNetExplorer
πJournal: Contemporary Educational Technology(I.F=3.68)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
CitNetExplorer
πJournal: Contemporary Educational Technology(I.F=3.68)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer