πA Network Science perspective of Graph Convolutional Networks: A survey
πJournal: FUTURE INTERNET
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
πJournal: FUTURE INTERNET
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
πNetwork Analysis of Road Traffic Crash and Rescue Operations in Federal Capital City
πJournal: International Journal of Geosciences (I.F=1.525)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Traffic
πJournal: International Journal of Geosciences (I.F=1.525)
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Traffic
πGraph-based Time-Series Anomaly Detection: A Survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #survey
πWomen financial inclusion research: a bibliometric and network analysis
πJournal: INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Women #financial #inclusion #bibliometric
πJournal: INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Women #financial #inclusion #bibliometric
πPredicting the establishment and removal of global trade relations for import and export of petrochemical products
πJournal: Energy (I.F=8.857)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #prediction #trade #petrochemical
πJournal: Energy (I.F=8.857)
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #prediction #trade #petrochemical
π Graph Theory Algorithms
π₯A complete overview of graph theory algorithms in computer science and mathematics.
π½Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph #course
π₯A complete overview of graph theory algorithms in computer science and mathematics.
π½Watch
π²Channel: @ComplexNetworkAnalysis
#video #Graph #course
Udemy
Graph Theory Algorithms
A complete overview of graph theory algorithms in computer science and mathematics.
πGraph Clustering with Graph Neural Networks
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Clustering #GNN
πPublish year: 2020
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Clustering #GNN
π4
ππNetwork Analysis Made Simple
π₯Network Analysis Made Simple is a collection of Jupyter notebooks designed to help you get up and running with the NetworkX package in the Python programming langauge. It's written by programmers for programmers, and will give you a basic introduction to graph theory, applied network science, and advanced topics to help kickstart your learning journey. There's even case studies to help those of you for whom example narratives help a ton!
π½Watch & study
π²Channel: @ComplexNetworkAnalysis
#video #Graph #course #python #code #ebook
π₯Network Analysis Made Simple is a collection of Jupyter notebooks designed to help you get up and running with the NetworkX package in the Python programming langauge. It's written by programmers for programmers, and will give you a basic introduction to graph theory, applied network science, and advanced topics to help kickstart your learning journey. There's even case studies to help those of you for whom example narratives help a ton!
π½Watch & study
π²Channel: @ComplexNetworkAnalysis
#video #Graph #course #python #code #ebook
π4
πCurriculum Graph Machine Learning: A Survey
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #Machine_Learning #Graph
πPublish year: 2023
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Survey #Machine_Learning #Graph
π2
πRelative, local and global dimension in complex networks
πJournal: NATURE COMMUNICATIONS (I.F=17.694)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Relative #local #global #dimension
πJournal: NATURE COMMUNICATIONS (I.F=17.694)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Relative #local #global #dimension
πGraph Algorithms with Python
π₯Technical paper
πIn this paper, the auther will take you through the implementation of Graph Algorithms with Python. As a data scientist, you should be well aware to find relationships among people by using the network they create within each other. So here the auther will take you through the Graph Algorithms you should know for Data Science using Python.
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #python #code
π₯Technical paper
πIn this paper, the auther will take you through the implementation of Graph Algorithms with Python. As a data scientist, you should be well aware to find relationships among people by using the network they create within each other. So here the auther will take you through the Graph Algorithms you should know for Data Science using Python.
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #python #code
thecleverprogrammer
Graph Algorithms with Python | Aman Kharwal
In this article, I will take you through the implementation of Graph Algorithms with Python. As a data scientist, you should be well aware
π7
π Knowledge Graph Seminar Session 2 (Spring 2020)
π₯Free recorded tutorial on Knowledge Graph.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #seminar
π₯Free recorded tutorial on Knowledge Graph.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #seminar
YouTube
CS 520: Knowledge Graph Seminar Session 2 (Spring 2020)
How to Create a Knowledge Graph?
π5
πA Mini Review of Node Centrality Metrics in Biological Networks
πJournal: International Journal of Network Dynamics and Intelligence
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #node_centrality #biological_network
πJournal: International Journal of Network Dynamics and Intelligence
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #node_centrality #biological_network
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πA social network analysis of two networks: Adolescent school network and Bitcoin trader network
πJournal: Decision Analytics Journal
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Adolescent #school #Bitcoin #trader
πJournal: Decision Analytics Journal
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Adolescent #school #Bitcoin #trader
π3
2017_Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
970.4 KB
πKnowledge Graph Embedding: Survey of Approaches and Applications
πJournal: IEEE Transactions on Knowledge and Data Engineering (I.F=9.235)
πPublish year: 2017
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #DeepLearning #Survey
πJournal: IEEE Transactions on Knowledge and Data Engineering (I.F=9.235)
πPublish year: 2017
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #DeepLearning #Survey
π3
π Machine Learning with Graphs: Introduction to Graph Neural Networks, Basics of Deep Learning, Deep Learning for Graphs
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯Starting from this lecture:
-we introduce the exciting technique of graph neural networks, that encodes node features with multiple layers of non-linear transformations based on graph structure. Graph neural networks have shown extraordinary performance in various tasks, and could tame the complex nature of graphs.
-we give a review of deep learning concepts and techniques that are essential for understanding graph neural networks. Starting from formulating machine learning as optimization problems, we introduce the concepts of objective function, gradient descent, non-linearity and back propagation.
-weβll give you an introduction of architecture of graph neural networks. One key idea covered in the lecture is that in GNNs, weβre generating node embeddings based on local network neighborhood. Instead of single layer, GNNs usually consist of arbitrary number of layers to integrate information from even larger contexts. We then introduce how we use GNNs to solve the optimization problems, and its powerful inductive capacity.
π½ Watch: part1 part2 part3
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯Starting from this lecture:
-we introduce the exciting technique of graph neural networks, that encodes node features with multiple layers of non-linear transformations based on graph structure. Graph neural networks have shown extraordinary performance in various tasks, and could tame the complex nature of graphs.
-we give a review of deep learning concepts and techniques that are essential for understanding graph neural networks. Starting from formulating machine learning as optimization problems, we introduce the concepts of objective function, gradient descent, non-linearity and back propagation.
-weβll give you an introduction of architecture of graph neural networks. One key idea covered in the lecture is that in GNNs, weβre generating node embeddings based on local network neighborhood. Instead of single layer, GNNs usually consist of arbitrary number of layers to integrate information from even larger contexts. We then introduce how we use GNNs to solve the optimization problems, and its powerful inductive capacity.
π½ Watch: part1 part2 part3
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 6.1 - Introduction to Graph Neural Networks
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3nvFQi3
Jure Leskovec
Computer Science, PhD
Previously we talked about some node embedding techniques that could learn task-independentβ¦
Jure Leskovec
Computer Science, PhD
Previously we talked about some node embedding techniques that could learn task-independentβ¦
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πA Survey on Knowledge Graphs: Representation, Acquisition, and Applications
πJournal: IEEE T NEUR NET LEAR (I.F=14.255)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Representation #Acquisition #Application #Survey
πJournal: IEEE T NEUR NET LEAR (I.F=14.255)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Representation #Acquisition #Application #Survey
πA Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
πJournal: SCI REP-UK (I.F=4.996)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Techniques #Inclusion #Domain #Knowledge #Deep_Neural_Networks #Review
πJournal: SCI REP-UK (I.F=4.996)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Techniques #Inclusion #Domain #Knowledge #Deep_Neural_Networks #Review
π Knowledge Graph Attention Network (KGAT)
π₯Free recorded tutorial on knowledge graph attention network.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #Attention
π₯Free recorded tutorial on knowledge graph attention network.
π½Watch
π±Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #Attention
YouTube
[Paper Review] Knowledge Graph Attention Network (KGAT)
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πInformation Diffusion Model in Twitter: A Systematic Literature Review
πJournal: INFORMATION
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Information #Diffusion #Twitter #Review
πJournal: INFORMATION
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Information #Diffusion #Twitter #Review
2020_In_search_of_network_resilience_An_optimization_based_view.pdf
825.6 KB
πIn search of network resilience: An optimization-based view
πJournal: wiley online library (I.F=15.153)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #network_resilience #optimization
πJournal: wiley online library (I.F=15.153)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #network_resilience #optimization
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