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Post #926 550
📄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
  • 👍 3
Post #925 600
📄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
  • 👍 3
Post #921 1.3K
🎞 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
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…
  • 👍 5
Post #917 6.86K
📄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
  • ❤ 3
  • 👍 1
Post #916 666
🎞 Network theory questions

💥Free recorded lectures.

💥Complete lectures on network analysis.

📽 Watch

📲Channel: @ComplexNetworkAnalysis

#video #lecture #Graph #Network
Post #912 1.17K
🎞 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.


📽 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…
  • 👍 4
  • ❤ 1
Post #907 653
🎞 Graph Analytics and Graph-based 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…
  • 👍 4
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