2020_Fraud_detection_A_systematic_literature_review_of_graph_based.pdf
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📄Fraud detection: A systematic literature review of graph-based anomaly detection approaches
📘Journal: Decision Support Systems (I.F=6.969)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fraud #Detection #Review #graph_based #anomaly
📘Journal: Decision Support Systems (I.F=6.969)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fraud #Detection #Review #graph_based #anomaly
📄Knowledge Graphs: Opportunities and Challenges
📘Journal: Artificial Intelligence Review (I.F=9.588)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Opportunities #Challenges
📘Journal: Artificial Intelligence Review (I.F=9.588)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Opportunities #Challenges
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📄Counterfactual Learning on Graphs: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Counterfactual_Learning #Graphs #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Counterfactual_Learning #Graphs #Survey
🎞 Overview of Complex Networks
💥Free recorded Tutorial on overview of complex networks
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Tutorial #Overview
💥Free recorded Tutorial on overview of complex networks
📽 Watch
📱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.
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🎓A comparison of visualisation techniques for complex networks
📘Master’s Thesis in Computer Science Royal Institute of Technology
🗓Publish year: 2016
📎Study Thesis
📱Channel: @ComplexNetworkAnalysis
#Thesis #comparison #visualisation #techniques
📘Master’s Thesis in Computer Science Royal Institute of Technology
🗓Publish year: 2016
📎Study Thesis
📱Channel: @ComplexNetworkAnalysis
#Thesis #comparison #visualisation #techniques
🎞 Machine Learning with Graphs: Theory of Graph Neural Networks
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
📽 Watch: part1 part2 part3
📝Slides
💻code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: Introduction to Graph Neural Networks, A Single Layer of a GNN, Stacking layers of a GNN
📽 Watch: part1 part2 part3
📝Slides
💻code
📲Channel: @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 Stanford’s 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 introduce…
Lecture 7.1 - A General Perspective on Graph Neural Networks
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
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🎞 Machine learning and link prediction
💥Free recorded Tutorial by Mark Needham & Jennifer Reif
💥Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_learning #link_prediction
💥Free recorded Tutorial by Mark Needham & Jennifer Reif
💥Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome
📽 Watch
📱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 the…
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📄Basic and Advanced Network Visualization with Gephi
💥Technical paper
📘 PDF
💻 data
📲Channel: @ComplexNetworkAnalysis
#tools #Gephi
💥Technical paper
💻 data
📲Channel: @ComplexNetworkAnalysis
#tools #Gephi
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📄 Literature review on the influence of social networks
📘Conference: The Fifth International Conference on Social Science
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Literature #influence #review
📘Conference: The Fifth International Conference on Social Science
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Literature #influence #review
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📕Networks, Crowds, and Markets:
Reasoning About a Highly Connected World
📝Authors: David Easley and Jon Kleinberg.
💥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.
🗓 publish year: 2010
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #network
Reasoning About a Highly Connected World
📝Authors: David Easley and Jon Kleinberg.
💥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.
🗓 publish year: 2010
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #network
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📄 Considering weights in real social networks: A review
📘Journal: Frontiers in Physics (I.F=3.718)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Considering #weights #review
📘Journal: Frontiers in Physics (I.F=3.718)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Considering #weights #review
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📕Network visualization with R
💥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.
📘 PDF
💻 code
🌐 Read online
📲Channel: @ComplexNetworkAnalysis
#book #R #code
💥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.
💻 code
🌐 Read online
📲Channel: @ComplexNetworkAnalysis
#book #R #code
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📄 Influential nodes identification in complex networks: a comprehensive literature review
📘Journal: Beni-Suef University Journal of Basic and Applied Sciences
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #nfluential #nodes #comprehensive #review
📘Journal: Beni-Suef University Journal of Basic and Applied Sciences
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #nfluential #nodes #comprehensive #review
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📄Graph Neural Networks for Text Classification: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #Text #Classification #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #Text #Classification #survey
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🎞 Social Network Analysis. Lecture4. Network structure and community detection
💥Free recorded Tutorial by Leonid E. Zhokov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection
💥Free recorded Tutorial by Leonid E. Zhokov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection
YouTube
Social Network Analysis. Lecture4. Network structure and community detection
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🎞 Machine Learning with Graphs: Theory of Graph Neural Networks
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: Graph argumentation for GNNs, Training graph neural networks, Setting up GNN prediction tasks
📽 Watch: part1 part2 part3
📝Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: Graph argumentation for GNNs, Training graph neural networks, Setting up GNN prediction tasks
📽 Watch: part1 part2 part3
📝Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.1 - Graph Augmentation for GNNs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XQPDGQ
Jure Leskovec
Computer Science, PhD
In this lecture, we will continue talking about the different design choices when training…
Jure Leskovec
Computer Science, PhD
In this lecture, we will continue talking about the different design choices when training…
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📄A comprehensive review on knowledge graphs for complex diseases
📘Journal: Briefings in Bioinformatics (I.F=13.994)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graphs #complex #diseases #review
📘Journal: Briefings in Bioinformatics (I.F=13.994)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #knowledge_graphs #complex #diseases #review
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