📄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
👍4
🎓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
👍4
🎞 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 ...
👍6👏1
📄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
👍3
📄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.
👍3
📄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
👍4
📄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
👍4👏1
📄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
👍6
🎞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…
👍4
📄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
👍4
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
👍4
🎞A Literature Review on Graph Neural Networks
💥Free recorded video on A Literature Review on Graph Neural Networks.
📄 first paper
📄 second paper
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #review
💥Free recorded video on A Literature Review on Graph Neural Networks.
📄 first paper
📄 second paper
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #GNN #review
YouTube
A Literature Review on Graph Neural Networks
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-04-15-spotlight
Discussion lead: Nabila Abraham
Survey papers:
1. Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
2. Representation…
Discussion lead: Nabila Abraham
Survey papers:
1. Graph Neural Networks: A Review of Methods and Applications
https://arxiv.org/abs/1812.08434
2. Representation…
👍4
🎞 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
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
👍5❤1
📄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
📘 Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
❤2👏1
🎞 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 #code #python #tensorflow
💥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 #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…
👍4
📄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
📘 Journal: Small Business Economics (I.F=6.4)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
👏2
📄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
📘 CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
👍7
📄Graph neural networks for materials science and
chemistry
📘 Journal: Communications Materials (I.F=7.8)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
chemistry
📘 Journal: Communications Materials (I.F=7.8)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
👍2
📄Network Medicine in Pathobiology
📘 journal: The American Journal of Pathology(I.F=5.1)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
📘 journal: The American Journal of Pathology(I.F=5.1)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
👍6
📄A Survey on the Recent Advances of Deep Community Detection
📘 Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
📘 Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
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