🎞 Graph Analytics and Graph-based Machine Learning
💥Free recorded tutorial by Dr Clair Sullivan.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph #Machine_Learning
💥Free recorded tutorial by Dr Clair Sullivan.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph #Machine_Learning
YouTube
Graph Analytics and Graph-based Machine Learning
Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data…
📄Introduction to Graph Machine Learning
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
📄Towards Data-centric Graph Machine Learning: Review and Outlook
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Review #Graph #Machine_Learning
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Review #Graph #Machine_Learning
Forwarded from Bioinformatics
📄Graph Visualization: Alternative Models Inspired by Bioinformatics
📘 Journal: Sensors (I.F=3.9)
🗓Publish year: 2023
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📲Channel: @Bioinformatics
#review #visualization
📘 Journal: Sensors (I.F=3.9)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @Bioinformatics
#review #visualization
🎞 IEICE English Webinar "Analysis of Complex Dynamical Behavior as a Temporal Network"
💥Free recorded course by Prof. Tohru Ikeguchi, Tokyo University of Science.
💥In this webinar, we will discuss the analysis of time-varying complex phenomena by considering measured contact data as a temporal network. Firstly, we will introduce some of the contact data currently recorded. Then, as an elemental technique for analyzing these contact data as temporal networks, we explain the analysis method for static networks. Secondly, we explain the importance of analyzing such contact data as temporal networks. We also explain how to transform contact data into temporal networks. Thirdly, we explain the distance measure between temporal networks in order to detect and quantify system dynamics from the transformed temporal networks. Furthermore, we explain how to analyze the dynamics of the changes in the contact data by converting the temporal changes in the distance into time series signals using the classical multidimensional scaling method. Finally, we conclude the methods for analyzing contact data as a temporal networks, and discuss a future direction of network analysis.
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📲Channel: @ComplexNetworkAnalysis
#video #webinar #Graph #Network #Anaysis
💥Free recorded course by Prof. Tohru Ikeguchi, Tokyo University of Science.
💥In this webinar, we will discuss the analysis of time-varying complex phenomena by considering measured contact data as a temporal network. Firstly, we will introduce some of the contact data currently recorded. Then, as an elemental technique for analyzing these contact data as temporal networks, we explain the analysis method for static networks. Secondly, we explain the importance of analyzing such contact data as temporal networks. We also explain how to transform contact data into temporal networks. Thirdly, we explain the distance measure between temporal networks in order to detect and quantify system dynamics from the transformed temporal networks. Furthermore, we explain how to analyze the dynamics of the changes in the contact data by converting the temporal changes in the distance into time series signals using the classical multidimensional scaling method. Finally, we conclude the methods for analyzing contact data as a temporal networks, and discuss a future direction of network analysis.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #webinar #Graph #Network #Anaysis
YouTube
IEICE English Webinar "Analysis of Complex Dynamical Behavior as a Temporal Network"
IEICE English Webinar Distinguished Lecturer Program Series July 2023
Analysis of Complex Dynamical Behavior as a Temporal Network
Lecturer: Prof. Tohru Ikeguchi, Tokyo University of Science
Biography:
Professor Tohru Ikeguchi received B.E., M.E., and Doctor…
Analysis of Complex Dynamical Behavior as a Temporal Network
Lecturer: Prof. Tohru Ikeguchi, Tokyo University of Science
Biography:
Professor Tohru Ikeguchi received B.E., M.E., and Doctor…
📄Graph Machine Learning: An Overview
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
Medium
Graph Machine Learning: An Overview
Key concepts for getting started
📄Graph Clustering with Graph Neural Networks
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Clustering
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Clustering
📄Visibility graph analysis for brain: scoping review
📘 journal: Frontiers in Neuroscience (I.F=5.152)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
📘 journal: Frontiers in Neuroscience (I.F=5.152)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
📄Machine Learning Algorithms
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Machine_learning
Graph Database & Analytics
Machine Learning Algorithms - Graph Database & Analytics
Get an introduction to machine learning and how new graph-based machine learning algorithms can be used to better analyze and understand data.
🎞 Machine Learning with Graphs: Community Detection in Network, Network Communities, Louvain Algorithm, Detecting Overlapping Communities
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, introduce methods that build on the intuitions presented in the previous part to identify clusters within networks. We define modularity score Q that measures how well a network is partitioned into communities. We also introduce null models to measure expected number of edges between nodes to compute the score. Using this idea, we then give a mathematical expression to calculate the modularity score. Finally, we can develop an algorithm to find communities by maximizing the modularity..
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Community_Detection
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, introduce methods that build on the intuitions presented in the previous part to identify clusters within networks. We define modularity score Q that measures how well a network is partitioned into communities. We also introduce null models to measure expected number of edges between nodes to compute the score. Using this idea, we then give a mathematical expression to calculate the modularity score. Finally, we can develop an algorithm to find communities by maximizing the modularity..
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Community_Detection
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 13.1 - Community Detection in Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Eu4Xss
Jure Leskovec
Computer Science, PhD
In this lecture, we first introduce the community structure of graphs and information…
Jure Leskovec
Computer Science, PhD
In this lecture, we first introduce the community structure of graphs and information…
📄Graph Data Structure And Algorithms
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Data_Structure #Algorithms
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Data_Structure #Algorithms
GeeksforGeeks
Graph Algorithms - GeeksforGeeks
Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.
📄Graph Theory
🧑🏻💼 author : Marc Lackenby
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📲Channel: @ComplexNetworkAnalysis
#paper #graph
🧑🏻💼 author : Marc Lackenby
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📲Channel: @ComplexNetworkAnalysis
#paper #graph
📄Graph Convolutional Networks: Introduction to GNNs
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GNN
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GNN
Medium
Graph Convolutional Networks: Introduction to GNNs
A step-by-step guide using PyTorch Geometric
📄Community Detection Algorithms in Healthcare
Applications: A Systematic Review
📘 journal: IEEE Access (I.F=3.9)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #review
Applications: A Systematic Review
📘 journal: IEEE Access (I.F=3.9)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #review
📄The Use of Graph Theory for Modeling and Analyzing the Structure of a Complex System, with the Example of an Industrial Grain Drying Line
📘 journal: processes (I.F=3.352)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #Analysis #Industrial_Grain_Drying_Line
📘 journal: processes (I.F=3.352)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #Analysis #Industrial_Grain_Drying_Line
2023 -A comprehensive survey of personal knowledge graphs.pdf
2.2 MB
📄 A comprehensive survey of personal knowledge graphs
📘 journal: Data Mining and Knowledge Discovery (I.F=7.8)
🗓Publish year: 2023
📲Channel: @ComplexNetworkAnalysis
#paper #survey #knowledge_graphs
📘 journal: Data Mining and Knowledge Discovery (I.F=7.8)
🗓Publish year: 2023
📲Channel: @ComplexNetworkAnalysis
#paper #survey #knowledge_graphs