🎓Graph entropy and related topics
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
👍6
📄Everything is Connected: Graph Neural Networks
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
👍1
📄Network Analysis of Time Series: Novel Approaches to Network Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📄A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📄A Comprehensive Survey on Graph Neural Networks
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
📄Summary of Static Graph Embedding Algorithms
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📄Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
❤1
📄Automated Machine Learning on Graphs: A Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🎞 Machine Learning with Graphs: Neural Subgraph Matching & Counting, Neural Subgraph Matching, Finding Frequent Subgraphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
YouTube
CS224W: Machine Learning with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jR7jK2
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
📄Recent Advances in Network-based Methods for
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Forwarded from Bioinformatics
🎓 Towards causality in gene regulatory network inference
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📄A Survey on Graph Classification and Link Prediction based on GNN
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
👍1
🎓Embedding of Dynamical Networks
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📄Graphs in computer graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics
📄Graph Viz: Exploring, Analyzing, and Visualizing Graphs and Networks with Gephi and ChatGPT
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #ChatGPT
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #ChatGPT
Open Data Science - Your News Source for AI, Machine Learning & more
Graph Viz: Exploring, Analyzing, and Visualizing Graphs and Networks with Gephi and ChatGPT
ChatGPT can do a lot with text, but how can it help with data viz? Here, we look at how you can analyze a global AI community using Gephi and ChatGPT.
👍3
📄Gephi Tutorial: How to use it for Network Analysis?
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
💥Technical paper
💥If you would like to get your hands dirty with some ONA software, we have prepared a simple Gephi tutorial to help you do basic organizational network analysis on a sample dataset. When you do it yourself, you get a better understanding of the logic of the analysis, the opportunities and limitations this open-source software provides, and a more meaningful interpretation of results, by using your context knowledge to better understand what the network statistics mean for the organizat .
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Gephi #Tutorial
👍3
📄Graph Neural Networks and Their Current Applications in Bioinformatics
📘journal: Frontiers in Genetics (I.F.=3.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#review #Graph_Neural_Networks #Application #Bioinformatics
📘journal: Frontiers in Genetics (I.F.=3.7)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#review #Graph_Neural_Networks #Application #Bioinformatics
👍1
📄Graph Learning and Its Applications: A Holistic Survey
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Graph #Applications
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Graph #Applications
🎞 Graph Analytics and Graph-based Machine Learning
💥Free recorded course by Clair Sullivan
💥Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Clair Sullivan
💥Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Graph Analytics and Graph-based Machine Learning
Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data…
📕Transportation Network Analysis
🗓Publish year: 2022
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Transportation
🗓Publish year: 2022
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Transportation