Network Analysis Resources & Updates - Telegram kanali @complexnetworkanalysis statistikasi va tahlili

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📄Theory of Graph Neural Networks: Representation and Learning 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #GNN #GRL

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📄A SURVEY OF GRAPH UNLEARNING 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Graph #Unlearning #Survey

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📄All you need to know about Graph Attention Networks 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #GAT #Coda

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📄Link Prediction in Social Networks: A Bibliometric Analysis and Review of Literature (1987-2021) 📘 Journal: Journal of Artificial Intelligence & Data Mining 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Link_Prediction #Bibliometric #review

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📄Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #code #python #GAT #Coda

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🎞 Promise and perils of population-scale social network analysis 💥Free recorded presentation by Frank Takes. 💥A relatively recently emerging line of research is devoted to the use of large-scale population register data to answer enduring questions in the realm of social science. In this presentation, it specifically delves into the network dimension of such data, focusing on information from the POPNET project, which covers more than 17 million people (i.e., the entire population of the Netherlands) and approximately 800 million family, household, school, work, and neighbor-to-neighbor connections. The presentation highlights the potential inherent in this comprehensive and curated social network data through illustrative examples of results related to issues such as social capital, segregation, and migration. Additionally, it will examine several methodological considerations and challenges related to under- and over-sampling of individual connections within opportunity structures, including findings on the validity of real-world skewed degree distributions. 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #Promise #perils #population_scale

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🎓A study of visibility graphs for time series representations 📘Bachelor’s Thesis, in the University Polytechnica de catalunya barcelonatech, Bergillos Varela, Carlos 🗓Publish year: 2023 📎Study Thesis 📲Channel: @ComplexNetworkAnalysis #Thesis #Visibility_Graph

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🎓A study of visibility graphs for time series representations 📘Bachelor’s Thesis, in the University Polytechnica de catalunya barcelonatech, Bergillos Varela, Carlos 🗓Publish year: 2020 📎Study Thesis 📲Channel: @ComplexNetworkAnalysis #Thesis #Visibility_Graph

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📄A Review of Link Prediction Applications in Network Biology 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Link_Prediction #Application #Biology #review

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📕Graph Representation Learning 💥Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis. 🌐 Read online 📲Channel: @ComplexNetworkAnalysis #book #GRL #GNN

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📄The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools 🗓Publish year: 2020 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Dimensions #Methods #Application #Software #Tools #Overview

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🎞 Understanding Graph Attention Networks 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #GNN #GAT #Graph

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🎞 Co-Authorship Network Analysis using GEPHI 💥This video is a part of one of the research articles that analyzes the collaboration patterns of the scientific co-authored article. 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #Co_Authorship #GEPHI

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📄A Review on Graph Neural Network Methods in Financial Applications 📘 Journal: Mental Health and Social Inclusion (IF=1.2) 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Financial #Application #review

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🎞 Analysis on Collaboration and Co-Authorship Network using Centrality Measures 💥Free recorded course 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #course #Co-Authorship #Centrality

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📄A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields 📘 Journal: Electronics (IF=2.9) 🗓Publish year: 2022 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Recommendation_Systems #Techniques #Application #survey

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📄A survey of graph neural network based recommendation in social networks 📘 Journal: Neurocomputing (IF=6) 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Recommendation #survey

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📄A survey of graph neural network based recommendation in social networks 📘 Journal: Neurocomputing (IF=6) 🗓Publish year: 2023 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Recommendation

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🎓Towards a deeper understanding of the Visibility Graph algorithm 📘Master’s Thesis, in the Delft University of Technolog, T.J. Alers 🗓Publish year: 2023 📎Study Thesis 📲Channel: @ComplexNetworkAnalysis #Thesis #Visibility_Graph

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🎞 Graph Neural Networks - Lecture 15 💥Free recorded tutorial by Manolis Klis 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #GNN