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πŸ“‘ Knowledge Graphs

πŸ“˜
Journal: ACM Computing Surveys(I.F.=14.324)
πŸ—“Publish year: 2021

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Survey #Knowledge_Graphs
2021_Graph_Signal_Processing,_Graph_Neural_Network_and_Graph_Learning.pdf
3.9 MB
πŸ“„Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review

πŸ“˜
Journal: IEEE REVIEWS IN BIOMEDICAL ENGINEERING (I.F=7.073)
πŸ—“
Publish year: 2021

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Graph_Signal_Processing #Graph_Neural_Network #Graph_Learning #Biological #Review
πŸ“„Community Detection Algorithms in Healthcare Applications: A Systematic Review

πŸ“˜
Journal: IEEE Access (I.F=3.476)
πŸ—“
Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #Review
2019-A review Knowledge reasoning over knowledge graph.pdf
2.2 MB
πŸ“‘ A review: Knowledge reasoning over knowledge graph

πŸ“˜
Journal: ACM Computing Surveys(I.F.=8.665)
πŸ—“Publish year: 2019

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #review #Knowledge_Graphs
πŸ“‘ Structure and dynamics of core/periphery networks

πŸ“˜Journal: Journal of Complex Networks(I.F.=1.984)
πŸ—“Publish year: 2013

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #review #periphery_networks
πŸ“‘ Exploring network structure, dynamics, and function using networkx

πŸ“˜Conference: conference: Exploring network structure, dynamics, and function using NetworkX

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #networkx
2020_Fraud_detection_A_systematic_literature_review_of_graph_based.pdf
1.4 MB
πŸ“„Fraud detection: A systematic literature review of graph-based anomaly detection approaches

πŸ“˜
Journal: Decision Support Systems (I.F=6.969)
πŸ—“
Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Fraud #Detection #Review #graph_based #anomaly
πŸ“„Knowledge Graphs: Opportunities and Challenges

πŸ“˜
Journal: Artificial Intelligence Review (I.F=9.588)
πŸ—“
Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Opportunities #Challenges
πŸ“„Programming Graphs in Python

πŸ’₯Technical paper

🌐 Study

πŸ“²Channel: @ComplexNetworkAnalysis

#paper #Graph #code #python
πŸ“„Counterfactual Learning on Graphs: A Survey

πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Counterfactual_Learning #Graphs #Survey
πŸŽ“A comparison of visualisation techniques for complex networks

πŸ“˜Master’s Thesis in Computer Science Royal Institute of Technology

πŸ—“Publish year: 2016

πŸ“ŽStudy Thesis

πŸ“±Channel: @ComplexNetworkAnalysis

#Thesis #comparison #visualisation #techniques
πŸ“„Programming Graphs in Python

πŸ’₯Technical paper

🌐 Study

πŸ“²Channel: @ComplexNetworkAnalysis

#paper #Graph #code #python
πŸ“„Basic and Advanced Network Visualization with Gephi

πŸ’₯Technical paper

πŸ“˜ PDF

πŸ’» data

πŸ“²Channel: @ComplexNetworkAnalysis

#tools #Gephi
πŸ“„ Literature review on the influence of social networks

πŸ“˜Conference: The Fifth International Conference on Social Science
πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Literature #influence #review
πŸ“•Networks, Crowds, and Markets:
Reasoning About a Highly Connected World

πŸ“Authors: David Easley and Jon Kleinberg.

πŸ’₯Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

πŸ—“
publish year: 2010
πŸ“–
Study book

πŸ“²Channel: @ComplexNetworkAnalysis

#book #network
πŸ“„ Considering weights in real social networks: A review

πŸ“˜Journal: Frontiers in Physics (I.F=3.718)
πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Considering #weights #review
πŸ“•Network visualization with R

πŸ’₯This is a comprehensive tutorial on network visualization with R. It covers data input and formats, visualization basics, parameters and layouts for one-mode and bipartite graphs; dealing with multiplex links, interactive and animated visualization for longitudinal networks; and visualizing networks on geographic maps. To follow the tutorial, download the code and data below and use R and RStudio. You can also check out the most recent versions of all my tutorials here.

πŸ“˜ PDF

πŸ’» code

🌐 Read online

πŸ“²Channel: @ComplexNetworkAnalysis

#book #R #code
2025/07/06 21:07:14
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