Network Analysis Resources & Updates - Статистика та аналітика Telegram каналу @complexnetworkanalysis

3 133

📄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

3 133

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

3 133

🎞 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

3 133

📄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

3 133

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

3 133

📄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

3 133

📄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

3 133

📄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

3 133

🎓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

3 133

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

3 133

📄Applications of social network analysis in promoting circular economy: a literature review 📘 Published by Vilnius Gediminas Technical University. 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #social_network #review #economy

3 133

📄A Review on Graph Neural Network Methods in Financial Applications 🗓Publish year: 2022 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #GNN #Financial #Applications #review

3 133

🎞 pytorch geometric tutorial: graph attention networks implementation 💥Free recorded course 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #course #Graph #GAT #code #python

3 133

📄GCN-tutorial 💥Technical paper 💥 Graph Convolutional Network. Perform convolution operations on a graph using the information embedded into each node. The main idea is to "look" at neighboor nodes and update the currently embedded information into a higher or lower dimensional space by performing a ReLU or softmax operation. 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #code #python #GCN #Coda

3 133

📄Do we need deep graph neural networks? 💥Technical paper 💥 One of the hallmarks of deep learning was the use of neural networks with tens or even hundreds of layers. In stark contrast, most of the architectures used in graph deep learning are shallow with just a handful of layers. In this post, I raise a heretical question: does depth in graph neural network architectures bring any advantage? 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph #DGNN

3 133

🎞 application of machine learning of traffic optimization 💥Free recorded course by Powel gora 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #course #Graph #Machine_Learning

3 133

📄Structure-oriented prediction in complex networks 📘 Journal: Physics Reports (IF=25.6) 🗓Publish year: 2018 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #prediction

3 133

🎞 Machine Learning with Graphs: Deep Generative Models for Graphs, Graph RNN: Generating Realistic Graphs, Scaling Up & Evaluating Graph Gen, Applications of Deep Graph Generation. 💥Free recorded course by Jure Leskovec, Computer Science, PhD 💥this lecture, focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in next parts introduce and focus on GraphRNN, one of the first deep generative models for graph; and finally, discuss GCPN, a deep graph generative model designed specifically for application to molecule generation. 📽 Watch: part1 part2 part3 part4 📲Channel: @ComplexNetworkAnalysis #video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph

3 133

📄Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence 📘 Journal: International journal of intelligent systems (IF=7) 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #GCN #overview

3 133

📄Graph Representation Learning 📘 journal: European Symposium on Artificial Neural Networks 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #GNN