Network Analysis Resources & Updates
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πŸ“„Wide Graph Neural Network

πŸ“˜Conference: The Eleventh International Conference on Learning Representations(ICLR 2023)
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Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Network #Wide
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πŸ“„Neural Network Optimization Based on Complex Network
Theory: A Survey

πŸ“˜ journal: MATHEMATICS (I.F=2.3)
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Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Neural_Network #Optimization #Survey
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🎞 GraphVar - Brain Network Analysis - Part 1/2
πŸ’₯Free recorded tutorial on Brain Network Analysis

πŸ”ΉThis is a demonstration of GraphVar and a walk through implemented functions. Brain Connectivity Toolbox.

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video #Brain_Network
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πŸ“„A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

πŸ—“Publish year: 2023

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Privacy #Graph_Neural_Network #Attacks #Preservation #Applications #Survey
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🎞 Benchmarking Graph Neural Network

πŸ’₯Free recorded tutorial on Benchmarking Graph Neural Network by Xavier Bresson, ​Yoshua Bengio| ICML Tutorial

🌐
Slides of this video

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video #Graph_Neural_Network
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πŸ“„Temporal Link Prediction: A Unified Framework, Taxonomy, and Review

πŸ—“Publish year: 2023

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Review #Graph #Link_Prediction
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πŸ“„Towards Data-centric Graph Machine Learning: Review and Outlook

πŸ—“Publish year: 2023

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Review #Graph #Machine_Learning
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Forwarded from Bioinformatics
πŸ“„Graph Visualization: Alternative Models Inspired by Bioinformatics

πŸ“˜ Journal: Sensors (I.F=3.9)
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Publish year: 2023

πŸ“Ž Study the paper

πŸ“²Channel: @Bioinformatics
#review #visualization
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🎞 IEICE English Webinar "Analysis of Complex Dynamical Behavior as a Temporal Network"

πŸ’₯Free recorded course by Prof. Tohru Ikeguchi, Tokyo University of Science.

πŸ’₯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.


πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #webinar #Graph #Network #Anaysis
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πŸ“„Graph Machine Learning: An Overview

πŸ’₯Technical paper

🌐 Study

πŸ“²Channel: @ComplexNetworkAnalysis

#paper #Graph #Machine_learning
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πŸ“„Graph Clustering with Graph Neural Networks

πŸ—“Publish year: 2023

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #GNN #Clustering
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🎞 Network theory questions

πŸ’₯Free recorded lectures.

πŸ’₯Complete lectures on network analysis.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #lecture #Graph #Network
πŸ“„Visibility graph analysis for brain: scoping review

πŸ“˜ journal: Frontiers in Neuroscience (I.F=5.152)
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Publish year: 2023

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #graph #brain #review
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