Network Analysis Resources & Updates - Estadísticas y analítica del canal de Telegram @complexnetworkanalysis

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📄A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges 📘 journal: ACM Computing Surveys (I.F=16.6) 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Graph #Counterfactual #Explanations #Evaluation #Challenges #survey

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📄 A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #survey #GNN #anomaly_detection #time_series

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📄Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey

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📄Influence maximization in social networks: a survey of behaviour-aware methods 📘 journal: Social Network Analysis and Mining (SNAM) (I.F=2.8) 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Influence #maximization #behaviour_aware #survey

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📄 A comprehensive survey of personal knowledge graphs 📘 journal: wiley(I.F=13.3) 🗓Publish year: 2023 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #survey #knowledge_graphs

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📄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 #analyis #Industrial_Grain_Drying_Line

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📄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

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📄Graph Convolutional Networks: Introduction to GNNs 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #GNN

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📄Graph Theory 🧑🏻‍💼 author : Marc Lackenby 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #graph

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📄Graph Data Structure And Algorithms 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #Data_Structure #Algorithms

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🎞 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

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📄Machine Learning Algorithms 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #Machine_learning

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📄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

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🎞 Network theory questions 💥Free recorded lectures. 💥Complete lectures on network analysis. 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #lecture #Graph #Network

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📄Graph Clustering with Graph Neural Networks 🗓Publish year: 2023 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Review #GNN #Clustering

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📄Graph Machine Learning: An Overview 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #Machine_learning

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📄 A comprehensive survey of personal knowledge graphs 📘 journal: wiley(I.F=13.3) 🗓Publish year: 2023 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #survey #knowledge_graphs

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🎞 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 #course #Graph #Network #Anaysis

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Repost from Bioinformatics

📄Graph Visualization: Alternative Models Inspired by Bioinformatics 📘 Journal: Sensors (I.F=3.9) 🗓Publish year: 2023 📎 Study the paper 📲Channel: @Bioinformatics #review #visualization

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📄Towards Data-centric Graph Machine Learning: Review and Outlook 🗓Publish year: 2023 📎 Study the paper 📲Channel: @ComplexNetworkAnalysis #paper #Review #Graph #Machine_Learning