📘Applied Social Network Analysis in Python by University of Michigan on Coursera
💥Free course by Daniel Romero
💻Codes
📲Channel: @ComplexNetworkAnalysis
#code #course #Social_Network #python
💥Free course by Daniel Romero
💻Codes
📲Channel: @ComplexNetworkAnalysis
#code #course #Social_Network #python
GitHub
GitHub - sambhipiyuushh/Applied-Social-Network-Analysis-in-Python-University-of-Michigan: Applied Social Network Analysis in Python…
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
📄A survey on graph-based methods for similarity searches in metric spaces
📘Journal: Information Systems(I.F=7.453)
🗓Publish year: 2021
📲Channel: @ComplexNetworkAnalysis
#paper #survey #graph
📘Journal: Information Systems(I.F=7.453)
🗓Publish year: 2021
📲Channel: @ComplexNetworkAnalysis
#paper #survey #graph
🎞 Getting Started with Network Analytics in Python
💥Free recorded tutorial by Eric Sims
🔹This tutorial is about DataTalks.Club (the place to talk about data)
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Python
💥Free recorded tutorial by Eric Sims
🔹This tutorial is about DataTalks.Club (the place to talk about data)
📽 Watch
📱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.linke…
- 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.linke…
Social_Network_Theory_in_Construction_Industry_A_Scientometric_Review.pdf
414.4 KB
📄Social Network Theory in Construction Industry: A Scientometric Review
📘Conference: Recent Trends in Civil Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Review #Social_Network
📘Conference: Recent Trends in Civil Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Review #Social_Network
On Anomaly Detection in Graphs as Node Classification.pdf
467.2 KB
📄On Anomaly Detection in Graphs as Node Classification
📘Conference: Big Data Management and Analysis for Cyber Physical Systems
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph
📘Conference: Big Data Management and Analysis for Cyber Physical Systems
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph
📄Graph Learning Approaches to Recommender Systems: A Review
📘Journal: Information Retrieval
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommender_Systems #Graph #review
📘Journal: Information Retrieval
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommender_Systems #Graph #review
🎉1
🎞 Graph Theory: Nearest Neighbor Algorithm (NNA)
💥Free recorded tutorial
🔹This tutorial is about Nearest neighbour algorithm, Travelling salesman problem, Heuristic, Hamiltonian path
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph
💥Free recorded tutorial
🔹This tutorial is about Nearest neighbour algorithm, Travelling salesman problem, Heuristic, Hamiltonian path
📽 Watch
📲Channel: @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: http://mathispower4u.com
Site: http://mathispower4u.com
👍1
📄An Introduction to Graph Theory
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
💥Technical paper
🌐 Study
📲Channel: @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.
📄A Note on Graph-Based Nearest Neighbor Search
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
👍4
📄Network Analysis for the Digital Humanities: Principles, Problems, Extensions
📘Journal: ISIS
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Digital #Humanities #Principles #Problems #Extensions
📘Journal: ISIS
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Digital #Humanities #Principles #Problems #Extensions
📄Network analysis on political election; populist vs social emergent behaviour
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
2022_Knowledge_Graphs_A_Practical_Review_of_the_Research_Landscape.pdf
510 KB
📄Knowledge Graphs: A Practical Review of the Research Landscape
📘Journal: INFORMATION
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
📘Journal: INFORMATION
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
📄Knowledge Graph Completion: A Bird’s Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
🎞 Machine Learning with Graphs: PageRank Random Walks and embedding
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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
📽 Watch: part1 part2 part3
📲Channel: @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 Stanford’s 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.…
Jure Leskovec
Computer Science, PhD
From previous lectures, we learn the use of graph representation learning for node classification.…
📄Taxonomy of Link Prediction for Social Network Analysis: A Review
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
📄Knowledge graph and knowledge reasoning: A systematic review
📘Journal: Journal of Electronic Science and Technology
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
📘Journal: Journal of Electronic Science and Technology
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
970.4 KB
📄Knowledge Graph Embedding: A Survey of Approaches and Applications
📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📄Gamification in education: A citation network analysis using
CitNetExplorer
📘Journal: Contemporary Educational Technology(I.F=3.68)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
CitNetExplorer
📘Journal: Contemporary Educational Technology(I.F=3.68)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
📄Complex Network Analysis of China National Standards for New Energy Vehicles
📘Journal: Sustainability(I.F=3.889)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: Sustainability(I.F=3.889)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
👨💻 MSc position at SBNA (Social & Biological Network Analysis) Lab
🇮🇷 Language: IR
🌐 Details
📲Channel: @ComplexNetworkAnalysis
🇮🇷 Language: IR
🌐 Details
📲Channel: @ComplexNetworkAnalysis
📄A Mini review of Node Centrality Metrics in Biological Networks
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #centrality #biological
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #centrality #biological
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