🎓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
📘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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2023_A_survey_of_graph_neural_network_based_recommendation_in_social.pdf
1.6 MB
📄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
📘 Journal: Neurocomputing (IF=6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Recommendation #survey
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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
📘 Journal: Electronics (IF=2.9)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Techniques #Application #survey
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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
💥Free recorded course
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Co_Authorship #Centrality
YouTube
Analysis on Collaboration and Co-Authorship Network using Centrality Measures
This is a presentation of a mini-paper I wrote on analysis on collaboration and co-authorship network of international Network Science researches by using the classical centrality measures and structural holes. The data set I used here is from M.E.J. Newman…
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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
📘 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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🎞 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
💥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
YouTube
Analysis on Collaboration and Co-Authorship Network using Centrality Measures
This is a presentation of a mini-paper I wrote on analysis on collaboration and co-authorship network of international Network Science researches by using the classical centrality measures and structural holes. The data set I used here is from M.E.J. Newman…
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🎞 Understanding Graph Attention Networks
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #GNN #GAT #Graph
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #GNN #GAT #Graph
YouTube
Understanding Graph Attention Networks
▬▬ Resources ▬▬▬▬▬▬▬▬▬▬
Paper: https://arxiv.org/pdf/1710.10903.pdf
Attention in NLP YouTube Series: https://www.youtube.com/watch?v=yGTUuEx3GkA (Rasa)
▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬
Field Of Fireflies by Purrple Cat | https://purrplecat.com
Music promoted…
Paper: https://arxiv.org/pdf/1710.10903.pdf
Attention in NLP YouTube Series: https://www.youtube.com/watch?v=yGTUuEx3GkA (Rasa)
▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬
Field Of Fireflies by Purrple Cat | https://purrplecat.com
Music promoted…
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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
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Dimensions #Methods #Application #Software #Tools #Overview
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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
💥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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📄A Review of Link Prediction Applications in Network Biology
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Application #Biology #review
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Application #Biology #review
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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
📘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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🎞 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
💥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
YouTube
Frank Takes - Promise and perils of population-scale social network analysis
Frank Takes, Leiden University
A relatively recently emerged line of research is dedicated to harnessing large-scale population register data to address enduring questions within the realm of social science. In this presentation, we will specifically delve…
A relatively recently emerged line of research is dedicated to harnessing large-scale population register data to address enduring questions within the realm of social science. In this presentation, we will specifically delve…
📄Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GAT #Coda
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GAT #Coda
towardsai.net
Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation | Towards AI
Author(s): Ebrahim Pichka Originally published on Towards AI. A detailed and illustrated walkthrough of the “Graph Attention Networks” paper by Veličković e ...
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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
📘 Journal: Journal of Artificial Intelligence & Data Mining
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Bibliometric #review
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📄All you need to know about Graph Attention Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Coda
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #GAT #Coda
Analytics India Magazine
All you need to know about Graph Attention Networks | AIM
A graph attention network can be explained as leveraging the attention mechanism in the graph neural networks so that we can address some of the shortcomings of the graph neural networks.
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📄A SURVEY OF GRAPH UNLEARNING
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Unlearning #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Unlearning #Survey
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📄Theory of Graph Neural Networks: Representation and Learning
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #GRL
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #GRL
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📄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
(GNNs): The Opportunities of GNNs in Power Electronics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Opportunities #Power_Electronics
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🎞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
💥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
YouTube
Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide
Organizers: Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
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📄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
📘 Journal: IEEE Transactions on Knowledge and Data Engineering
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
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Network_and_Content_Analysis_in_an_Online_Community_Discourse.pdf
292.2 KB
📕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
💥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
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