Network Analysis Resources & Updates

📑 Structure and Dynamics of Information in Networks 📔Booklet: David Kempe, 2021 📎Study Booklet 📱Channel: @ComplexNetworkAnalysis #Booklet #Structure #Dynamics #Information

📄Graph neural networks for affective social media: A comprehensive overview 📘Conference: THECOG 🗓Publish year: 2022 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #social_media #overview

🎞 How to Analyze Social Media Networks with Kumu and NodeXL 💥Free recorded tutorial 🔹NodeXL and Kumu are two powerful free tools for social network analysis. NodeXL is excellent for gathering social media data but it is more challenging (for beginners) to generate understandable visualizations. Kumu on the other hand is an excellent tool for social network analysis, but you can't collect social media data using the tool. Here's how to combine the two together 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #Kumu #NodeXL

📄The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research 📘Journal: PSYCHOLOGICAL MEDICINE (I.F=10.592) 🗓Publish year: 2020 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #psychopathology #review

📄Investigating Fake and Reliable News Sources Using Complex Networks Analysis 📘Journal: Frontiers in Physics (I.F=3.56) 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Fake_news #Reliable_News

📄A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis 📘Journal: Physica A: Statistical Mechanics and its Applications (I.F=3.263) 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #traffic

📄Bipartite graphs in systems biology and medicine: a survey of methods and applications 📘Journal: GIGASCIENCE (I.F=7.658) 🗓Publish year: 2018 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #biology #medicine #applications #survey

📄Tensor decomposition for analysing time-evolving social networks: an overview 📘Journal: Artificial Intelligence Review (I.F=9.588) 🗓Publish year: 2020 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Tensor #time_evolving #overview

🎞 Machine Learning with Graphs: Traditional Feature-based Methods 💥Free recorded course by Jure Leskovec, Computer Science, PhD 🔸 Encoder-decoder framework: ▫️ Encoder: embedding lookup ▫️ Decoder: predict score based on embedding to match node similarity 🔸 Node similarity measure: (biased) random walk ▫️ Examples: DeepWalk, Node2Vec 🔸 Extension to Graph embedding: ▫️Node embedding aggregation and Anonymous Walk Embeddings 📽 Watch: part1 part2 part3 📜 Slides 📲Channel: @ComplexNetworkAnalysis #video #course #Graph #Machine_Learning

📄Network Analysis of Time Series: Novel Approaches to Network Neuroscience 📘Journal: Frontiers in Neuroscience (I.F=4.501) 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Brain #Neuroscience

📄Network Analysis of Time Series: Novel Approaches to Network Neuroscience 📘Journal:Frontiers in Neuroscience (I.F=4.501) 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Brain #Neuroscience

🎞 Network Analysis with Python 💥Free recorded tutorial 🔹This tutorial walks you through the process of loading network data into Python and shows you how to run a few network metrics 📽 Watch 📱Channel: @ComplexNetworkAnalysis #video #python