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
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?
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
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
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
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¦
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)
Knowledge Graph Attention Networkμ λν λ°νμλ£μ
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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
πNetwork visualization tools and libraries
π₯Technical article
π Study
π²Channel: @ComplexNetworkAnalysis
#visualization
π₯Technical article
π Study
π²Channel: @ComplexNetworkAnalysis
#visualization
πA Social Network Analysis of Occupational Segregation
πJournal: journal of economic dynamics and control (I.F=1.53)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Network
πJournal: journal of economic dynamics and control (I.F=1.53)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Network
πData Analysis in Social Networks for Agribusiness: A Systematic Review
πJournal: IEEE Access(I.F=4.34)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Review
πJournal: IEEE Access(I.F=4.34)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Review
2020_Credit_risk_and_financial_integration_An_application_of_network.pdf
898.7 KB
πCredit risk and financial integration: An application of network analysis
πJournal: International Review of Financial Analysis(I.F=8.235)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #financial #application
πJournal: International Review of Financial Analysis(I.F=8.235)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #financial #application
πInterpretable and Effortless Techniques for Social Network Analysis
πPhDβs Dissertation, in Universidad de Granada, department of computer science and artificial intelligence, by Manuel Francisco Aparicio.
πPublish year: 2022
πStudy Dissertation
π²Channel: @ComplexNetworkAnalysis
#Dissertation #Social_Network #Techniques
πPhDβs Dissertation, in Universidad de Granada, department of computer science and artificial intelligence, by Manuel Francisco Aparicio.
πPublish year: 2022
πStudy Dissertation
π²Channel: @ComplexNetworkAnalysis
#Dissertation #Social_Network #Techniques
Financial Crisis and Global Governance A Network Analysis.pdf
155.8 KB
πFinancial Crisis and Global Governance: A Network Analysis
πAuthor: Andrew Sheng
π₯This chapter attempts to use network theory, drawn from recent work in sociology, engineering, and biological systems, to suggest that the current crisis should be viewed as a network crisis. Global fi nancial markets act as complex, scale-free, evolving networks that possess key characteristics requiring network management if they are to function with stability.
π publish year: 2010
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #network
πAuthor: Andrew Sheng
π₯This chapter attempts to use network theory, drawn from recent work in sociology, engineering, and biological systems, to suggest that the current crisis should be viewed as a network crisis. Global fi nancial markets act as complex, scale-free, evolving networks that possess key characteristics requiring network management if they are to function with stability.
π publish year: 2010
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #network
International_trade_and_financial_integration_a_weighted_network.pdf
289.6 KB
πInternational trade and financial integration: a weighted network analysis
πJournal: Quantitative Finance(I.F=2.13)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
πJournal: Quantitative Finance(I.F=2.13)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
π Webinar: Social Network Analysis: Fundamental Concepts
π₯Free recorded Webinar
π₯This free webinar, organised by the UK Data Service, is the first in a series of three on understanding and using SNA methods for social science research purposes. In this webinar they cover the fundamental concepts and terms underpinning SNA, and demonstrate how network data is structured and differs from more traditional social science datasets (e.g. social surveys). We will also outline a simple analysis of social network data using the Python programming language. As a result of attending this webinar, participants will possess the necessary knowledge and vocabulary to undertake a SNA research project.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Webinar #Social_Network
π₯Free recorded Webinar
π₯This free webinar, organised by the UK Data Service, is the first in a series of three on understanding and using SNA methods for social science research purposes. In this webinar they cover the fundamental concepts and terms underpinning SNA, and demonstrate how network data is structured and differs from more traditional social science datasets (e.g. social surveys). We will also outline a simple analysis of social network data using the Python programming language. As a result of attending this webinar, participants will possess the necessary knowledge and vocabulary to undertake a SNA research project.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Webinar #Social_Network
YouTube
Webinar: Social Network Analysis: Fundamental Concepts
Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. Thus, computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientistβs toolkit.β¦
πMulti-Agent Systems and Complex Networks: Review and Applications in Systems Engineering
πJournal: PROCESSES(I.F=3.352)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Multi_Agent #Applications #Systems #Engineering #Review
πJournal: PROCESSES(I.F=3.352)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Multi_Agent #Applications #Systems #Engineering #Review