📄A Mini Review of Node Centrality Metrics in Biological Networks
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #node_centrality #biological_network
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #node_centrality #biological_network
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📄A social network analysis of two networks: Adolescent school network and Bitcoin trader network
📘Journal: Decision Analytics Journal
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #Adolescent #school #Bitcoin #trader
📘Journal: Decision Analytics Journal
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Adolescent #school #Bitcoin #trader
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2017_Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
970.4 KB
📄Knowledge Graph Embedding: Survey of Approaches and Applications
📘Journal: IEEE Transactions on Knowledge and Data Engineering (I.F=9.235)
🗓Publish year: 2017
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #DeepLearning #Survey
📘Journal: IEEE Transactions on Knowledge and Data Engineering (I.F=9.235)
🗓Publish year: 2017
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #DeepLearning #Survey
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🎞 Machine Learning with Graphs: Introduction to Graph Neural Networks, Basics of Deep Learning, Deep Learning for Graphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
-we’ll give you an introduction of architecture of graph neural networks. One key idea covered in the lecture is that in GNNs, we’re 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.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
-we’ll give you an introduction of architecture of graph neural networks. One key idea covered in the lecture is that in GNNs, we’re 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.
📽 Watch: part1 part2 part3
📲Channel: @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 Stanford’s 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-independent…
Jure Leskovec
Computer Science, PhD
Previously we talked about some node embedding techniques that could learn task-independent…
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📄A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
📘Journal: IEEE T NEUR NET LEAR (I.F=14.255)
🗓Publish year: 2021
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📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Representation #Acquisition #Application #Survey
📘Journal: IEEE T NEUR NET LEAR (I.F=14.255)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Representation #Acquisition #Application #Survey
📄A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
📘Journal: SCI REP-UK (I.F=4.996)
🗓Publish year: 2021
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📱Channel: @ComplexNetworkAnalysis
#paper #Techniques #Inclusion #Domain #Knowledge #Deep_Neural_Networks #Review
📘Journal: SCI REP-UK (I.F=4.996)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Techniques #Inclusion #Domain #Knowledge #Deep_Neural_Networks #Review
🎞 Knowledge Graph Attention Network (KGAT)
💥Free recorded tutorial on knowledge graph attention network.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #Attention
💥Free recorded tutorial on knowledge graph attention network.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #Attention
YouTube
[Paper Review] Knowledge Graph Attention Network (KGAT)
Knowledge Graph Attention Network에 대한 발표자료입니다.
📄Information Diffusion Model in Twitter: A Systematic Literature Review
📘Journal: INFORMATION
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #Information #Diffusion #Twitter #Review
📘Journal: INFORMATION
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Information #Diffusion #Twitter #Review
2020_In_search_of_network_resilience_An_optimization_based_view.pdf
825.6 KB
📄In search of network resilience: An optimization-based view
📘Journal: wiley online library (I.F=15.153)
🗓Publish year: 2020
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📲Channel: @ComplexNetworkAnalysis
#paper #network_resilience #optimization
📘Journal: wiley online library (I.F=15.153)
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #network_resilience #optimization
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🖍Network visualization tools and libraries
💥Technical article
📎 Study
📲Channel: @ComplexNetworkAnalysis
#visualization
💥Technical article
📎 Study
📲Channel: @ComplexNetworkAnalysis
#visualization
📄A Social Network Analysis of Occupational Segregation
📘Journal: journal of economic dynamics and control (I.F=1.53)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network
📘Journal: journal of economic dynamics and control (I.F=1.53)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network
📄Data Analysis in Social Networks for Agribusiness: A Systematic Review
📘Journal: IEEE Access(I.F=4.34)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Review
📘Journal: IEEE Access(I.F=4.34)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Review
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2020_Credit_risk_and_financial_integration_An_application_of_network.pdf
898.7 KB
📄Credit risk and financial integration: An application of network analysis
📘Journal: International Review of Financial Analysis(I.F=8.235)
🗓Publish year: 2020
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📲Channel: @ComplexNetworkAnalysis
#paper #financial #application
📘Journal: International Review of Financial Analysis(I.F=8.235)
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #financial #application
🎓Interpretable and Effortless Techniques for Social Network Analysis
📘PhD’s Dissertation, in Universidad de Granada, department of computer science and artificial intelligence, by Manuel Francisco Aparicio.
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Social_Network #Techniques
📘PhD’s Dissertation, in Universidad de Granada, department of computer science and artificial intelligence, by Manuel Francisco Aparicio.
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Social_Network #Techniques
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Financial Crisis and Global Governance A Network Analysis.pdf
155.8 KB
📕Financial Crisis and Global Governance: A Network Analysis
📝Author: Andrew Sheng
💥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.
🗓 publish year: 2010
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #network
📝Author: Andrew Sheng
💥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.
🗓 publish year: 2010
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #network
International_trade_and_financial_integration_a_weighted_network.pdf
289.6 KB
📄International trade and financial integration: a weighted network analysis
📘Journal: Quantitative Finance(I.F=2.13)
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
📘Journal: Quantitative Finance(I.F=2.13)
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
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🎞 Webinar: Social Network Analysis: Fundamental Concepts
💥Free recorded Webinar
💥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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Webinar #Social_Network
💥Free recorded Webinar
💥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.
📽 Watch
📲Channel: @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 scientist’s toolkit.…
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📄Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering
📘Journal: PROCESSES(I.F=3.352)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Multi_Agent #Applications #Systems #Engineering #Review
📘Journal: PROCESSES(I.F=3.352)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Multi_Agent #Applications #Systems #Engineering #Review
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📄A Review of Knowledge Graph Completion
📘Journal: Information (I.F=3.38)
🗓 publish year: 2022
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📲Channel: @ComplexNetworkAnalysis
#paper #Review #Knowledge_Graph
📘Journal: Information (I.F=3.38)
🗓 publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Review #Knowledge_Graph
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Credit_risk_and_financial_integration_An_application_of_network.pdf
835 KB
📄Credit risk and financial integration: An application of network analysis
📘Journal: International Review of Financial Analysis (I.F=8.235)
🗓 publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
📘Journal: International Review of Financial Analysis (I.F=8.235)
🗓 publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #financial #trade #weighted_network
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📄Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
📘Journal: IEEE ACCESS (I.F=3.476)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Anomaly #Graph_Neural_Networks #Status #Challenges
📘Journal: IEEE ACCESS (I.F=3.476)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Anomaly #Graph_Neural_Networks #Status #Challenges