Network Analysis Resources & Updates
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📄Generative Diffusion Models on Graphs: Methods and Applications
📘 CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
📄Social network research in the family business literature: a review and integration
📘 Journal: Small Business Economics (I.F=6.4)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
🎞 Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
💥Free recorded course by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
💥Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN
📄Recommending on graphs: a comprehensive review from a data perspective
📘 Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
🎞A Literature Review on Graph Neural Networks
💥Free recorded video on A Literature Review on Graph Neural Networks.
📄The first paper
📄 The second paper
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #review
📕Network and Content Analysis in an Online Community Discourse
💥The aim of this paper is to study interaction patterns among the members of a community of practice within the Dutch police organization and the way they share and construct knowledge together. The online discourse between 46 members, using First Class, formed the basis for this study. Social Network Analysis and content analysis were used to analyze the data. The results show that the interaction patterns between the members are rather centralized and that the network is relatively dense. Most of the members are involved within the discourse but person to person communication is still rather high. Content analysis revealed that discourse is focused on sharing and comparing information.
🌐 Read online
📲Channel: @ComplexNetworkAnalysis
#book_Chapter
📄Deep Learning on Graphs: A Survey
📘 Journal: IEEE Transactions on Knowledge and Data Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
🎞Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
💥Free recorded Tutorial by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin.
💥Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Tutorial #GNN #code #python #TensorFlow
📄From Graph Theory to Graph Neural Networks
(GNNs): The Opportunities of GNNs in Power Electronics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
📄Theory of Graph Neural Networks: Representation and Learning
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #GRL
📄A SURVEY OF GRAPH UNLEARNING
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Unlearning #Survey
📄All you need to know about Graph Attention Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Coda
📄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
📄Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GAT #Coda
🎞 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
🎓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
🎓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
📄A Review of Link Prediction Applications in Network Biology
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Application #Biology #review
📕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
