π Knowledge Graphs
πJournal: ACM Computing Surveys(I.F.=14.324)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #Knowledge_Graphs
πJournal: ACM Computing Surveys(I.F.=14.324)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #Knowledge_Graphs
2021_Graph_Signal_Processing,_Graph_Neural_Network_and_Graph_Learning.pdf
3.9 MB
πGraph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review
πJournal: IEEE REVIEWS IN BIOMEDICAL ENGINEERING (I.F=7.073)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph_Signal_Processing #Graph_Neural_Network #Graph_Learning #Biological #Review
πJournal: IEEE REVIEWS IN BIOMEDICAL ENGINEERING (I.F=7.073)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Graph_Signal_Processing #Graph_Neural_Network #Graph_Learning #Biological #Review
πCommunity Detection Algorithms in Healthcare Applications: A Systematic Review
πJournal: IEEE Access (I.F=3.476)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #Review
πJournal: IEEE Access (I.F=3.476)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #Review
2019-A review Knowledge reasoning over knowledge graph.pdf
2.2 MB
π A review: Knowledge reasoning over knowledge graph
πJournal: ACM Computing Surveys(I.F.=8.665)
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #review #Knowledge_Graphs
πJournal: ACM Computing Surveys(I.F.=8.665)
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #review #Knowledge_Graphs
π Structure and dynamics of core/periphery networks
πJournal: Journal of Complex Networks(I.F.=1.984)
πPublish year: 2013
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #review #periphery_networks
πJournal: Journal of Complex Networks(I.F.=1.984)
πPublish year: 2013
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #review #periphery_networks
π Exploring network structure, dynamics, and function using networkx
πConference: conference: Exploring network structure, dynamics, and function using NetworkX
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #networkx
πConference: conference: Exploring network structure, dynamics, and function using NetworkX
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #networkx
2020_Fraud_detection_A_systematic_literature_review_of_graph_based.pdf
1.4 MB
πFraud detection: A systematic literature review of graph-based anomaly detection approaches
πJournal: Decision Support Systems (I.F=6.969)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Fraud #Detection #Review #graph_based #anomaly
πJournal: Decision Support Systems (I.F=6.969)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Fraud #Detection #Review #graph_based #anomaly
πKnowledge Graphs: Opportunities and Challenges
πJournal: Artificial Intelligence Review (I.F=9.588)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Opportunities #Challenges
πJournal: Artificial Intelligence Review (I.F=9.588)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Opportunities #Challenges
πCounterfactual Learning on Graphs: A Survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Counterfactual_Learning #Graphs #Survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Counterfactual_Learning #Graphs #Survey
π Overview of Complex Networks
π₯Free recorded Tutorial on overview of complex networks
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Tutorial #Overview
π₯Free recorded Tutorial on overview of complex networks
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Tutorial #Overview
YouTube
Overview of Complex Networks
Episode 10, Principles of Complex Systems, Spring 2013, University of Vermont.
Overview of Complex Networks.
Overview of Complex Networks.
πA comparison of visualisation techniques for complex networks
πMasterβs Thesis in Computer Science Royal Institute of Technology
πPublish year: 2016
πStudy Thesis
π±Channel: @ComplexNetworkAnalysis
#Thesis #comparison #visualisation #techniques
πMasterβs Thesis in Computer Science Royal Institute of Technology
πPublish year: 2016
πStudy Thesis
π±Channel: @ComplexNetworkAnalysis
#Thesis #comparison #visualisation #techniques
π Machine Learning with Graphs: Theory of Graph Neural Networks
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
π½ Watch: part1 part2 part3
πSlides
π»code
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
π½ Watch: part1 part2 part3
πSlides
π»code
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.1 - A general Perspective on GNNs
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BjIqNd
Lecture 7.1 - A General Perspective on Graph Neural Networks
Jure Leskovec
Computer Science, PhD
In this lecture, we introduceβ¦
Lecture 7.1 - A General Perspective on Graph Neural Networks
Jure Leskovec
Computer Science, PhD
In this lecture, we introduceβ¦
π Machine learning and link prediction
π₯Free recorded Tutorial by Mark Needham & Jennifer Reif
π₯Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Machine_learning #link_prediction
π₯Free recorded Tutorial by Mark Needham & Jennifer Reif
π₯Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Machine_learning #link_prediction
YouTube
Machine learning and link prediction by Mark Needham & Jennifer Reif
Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack theβ¦
πBasic and Advanced Network Visualization with Gephi
π₯Technical paper
π PDF
π» data
π²Channel: @ComplexNetworkAnalysis
#tools #Gephi
π₯Technical paper
π PDF
π» data
π²Channel: @ComplexNetworkAnalysis
#tools #Gephi
π Literature review on the influence of social networks
πConference: The Fifth International Conference on Social Science
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Literature #influence #review
πConference: The Fifth International Conference on Social Science
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Literature #influence #review
πNetworks, Crowds, and Markets:
Reasoning About a Highly Connected World
πAuthors: David Easley and Jon Kleinberg.
π₯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.
π publish year: 2010
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #network
Reasoning About a Highly Connected World
πAuthors: David Easley and Jon Kleinberg.
π₯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.
π publish year: 2010
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #network
π Considering weights in real social networks: A review
πJournal: Frontiers in Physics (I.F=3.718)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Considering #weights #review
πJournal: Frontiers in Physics (I.F=3.718)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Considering #weights #review
πNetwork visualization with R
π₯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.
π PDF
π» code
π Read online
π²Channel: @ComplexNetworkAnalysis
#book #R #code
π₯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.
π PDF
π» code
π Read online
π²Channel: @ComplexNetworkAnalysis
#book #R #code