📑Graph-powered learning methods in the Internet of Things: A survey
📘journal: Machine Learning with Applications (I.F=3.203)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #IOT #Survey
📘journal: Machine Learning with Applications (I.F=3.203)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #IOT #Survey
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From_Social_Networks_to_Time_Series_Methods_and_Applications_1.pdf
1 MB
📕From Social Networks to Time Series: Methods and Applications
📝Authors: Tongfeng Weng, Yaofeng Zhang, Pan Hui.
🗓 publish year: 2017
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #Social_Network #Application
📝Authors: Tongfeng Weng, Yaofeng Zhang, Pan Hui.
🗓 publish year: 2017
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #Social_Network #Application
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🎞 Machine Learning with Graphs: Theory of Graph Neural Networks
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: How expensive are graph neural networks, designing the most powerful GNNs.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: How expensive are graph neural networks, designing the most powerful GNNs.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 9.1 - How Expressive are Graph Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GwTmur
Jure Leskovec
Computer Science, PhD
In this lecture, we provide a theoretical framework to analyze the expressive power…
Jure Leskovec
Computer Science, PhD
In this lecture, we provide a theoretical framework to analyze the expressive power…
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📄 Precision medicine — networks to the rescue
📘journal: Current Opinion in Biotechnology (COBIOT) (I.F=10.279)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #iPrecision #medicine #networks #rescue
📘journal: Current Opinion in Biotechnology (COBIOT) (I.F=10.279)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #iPrecision #medicine #networks #rescue
👍4
📄 Role-Aware Information Spread in Online Social Networks
📘journal: ENTROPY-SWITZ (I.F=2.738)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Role_Aware #Information #Spread #Online
📘journal: ENTROPY-SWITZ (I.F=2.738)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Role_Aware #Information #Spread #Online
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📑A Review of Graph and Network Complexity from an Algorithmic Information Perspective
📘journal: Entropy (I.F=2.738)
🗓Publish year: 2018
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
📘journal: Entropy (I.F=2.738)
🗓Publish year: 2018
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
👍5👏1
📄 A survey of community detection methods in multilayer networks
📘journal: Data Mining and Knowledge Discovery (I.F=5.406)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #community_detection #methods #multilayer #survey
📘journal: Data Mining and Knowledge Discovery (I.F=5.406)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #community_detection #methods #multilayer #survey
👍1
📑Considering weights in real social networks: A review
📘journal: Frontiers in Physics(I.F=3.718)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_networks #Review
📘journal: Frontiers in Physics(I.F=3.718)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_networks #Review
👍2👏1
📄 Community detection for multilayer weighted networks
📘journal: Information Sciences(I.F=8.233)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Community_detection #multilayer #weighted_networks
📘journal: Information Sciences(I.F=8.233)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Community_detection #multilayer #weighted_networks
👍2
📑Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_learning #Application
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_learning #Application
👍4
🎞 Network Analysis of Organizations
💥Free recorded course by professor Daniel A. McFarland.
💥In this course, we will describe how organization’s researchers look at social networks within organizations. In addition, we will describe how some theorists contend there is a network form of organization that is distinct from hierarchical organizations and markets. So we will relate two perspectives: a purely analytic one that describes networks within organizations, and a theoretical one concerning a prescribed form of inter- organizational association that can result in better outputs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network
💥Free recorded course by professor Daniel A. McFarland.
💥In this course, we will describe how organization’s researchers look at social networks within organizations. In addition, we will describe how some theorists contend there is a network form of organization that is distinct from hierarchical organizations and markets. So we will relate two perspectives: a purely analytic one that describes networks within organizations, and a theoretical one concerning a prescribed form of inter- organizational association that can result in better outputs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network
👏2
📄 Review on Learning and Extracting Graph Features for Link Prediction
📘journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Learning #Extracting #Graph #Features #Link_Prediction #review
📘journal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Learning #Extracting #Graph #Features #Link_Prediction #review
👍1
🎞 Network Theory
💥Free recorded course.
💥This lecture will discuss Network Theory:
Part I – Static networks:
🔸Understand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
🔸Understand different topologies and how they affect the network:
-Random
-Preferential
🔸Know the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
🔸Understand basic network evolution processes:
-Small world networks
Part II – Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
🔸Networks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
🔸Measuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
🔸Processes on networks:
-Avalanche models
-Metcalfe’s law
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
💥Free recorded course.
💥This lecture will discuss Network Theory:
Part I – Static networks:
🔸Understand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
🔸Understand different topologies and how they affect the network:
-Random
-Preferential
🔸Know the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
🔸Understand basic network evolution processes:
-Small world networks
Part II – Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
🔸Networks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
🔸Measuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
🔸Processes on networks:
-Avalanche models
-Metcalfe’s law
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
TU Delft OCW
Network Theory - TU Delft OCW
👍3
📄 A survey of graph neural network based recommendation in social networks
📘journal: Neurocomputing(I.F=5.779)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #graph_neural_network #Extracting #recommendation #survey
📘journal: Neurocomputing(I.F=5.779)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #graph_neural_network #Extracting #recommendation #survey
👍1
📄 Critical Review of Social Network Analysis Applications in Complex Project Management
📘journal: Journal of Management in Engineerin(I.F=6.415)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Critical #Applications #Complex_Project #Management #Review
📘journal: Journal of Management in Engineerin(I.F=6.415)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Critical #Applications #Complex_Project #Management #Review
👍2
📑Exploring social-emotional learning, school climate, and social network analysis
📘journal: Journal of Community Psychology(I.F=2.297)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
📘journal: Journal of Community Psychology(I.F=2.297)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
👍2
🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pNkBLE
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
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📄 Bibliometric review of ecological network analysis: 2010–2016
📘journal: ECOLOGICAL MODELLING(I.F=3.512)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #ecological #review
📘journal: ECOLOGICAL MODELLING(I.F=3.512)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #ecological #review
📑Clustering and link prediction for mesoscopic COVID-19 transmission networks in Republic of Korea
📘journal: ChaosI.F=3.436)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #link_prediction #Clustering
📘journal: ChaosI.F=3.436)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #link_prediction #Clustering
👍3
📑Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network
📘Conference: Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
💥Social network theory is becoming more and more significant in social science, and the centrality measure is underlying this burgeoning theory. In perspective of social network, individuals, organizations, companies etc. are like nodes in the network, and centrality is used to measure these nodes’ power, activity, communication convenience and so on. Meanwhile, degree centrality, betweenness centrality and closeness centrality are the popular detailed measurements. Thispaper presents these 3 centrality in-depth, from principle to algorithm, and prospect good in the future use.
🗓Publish year: 2017
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Centrality #Social_Network #Clustering
📘Conference: Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
💥Social network theory is becoming more and more significant in social science, and the centrality measure is underlying this burgeoning theory. In perspective of social network, individuals, organizations, companies etc. are like nodes in the network, and centrality is used to measure these nodes’ power, activity, communication convenience and so on. Meanwhile, degree centrality, betweenness centrality and closeness centrality are the popular detailed measurements. Thispaper presents these 3 centrality in-depth, from principle to algorithm, and prospect good in the future use.
🗓Publish year: 2017
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Centrality #Social_Network #Clustering
Atlantis-Press
Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network | Atlantis Press
Social network theory is becoming more and more significant in social science, and the centrality measure is underlying this burgeoning theory. In perspective of social network, individuals, organizations, companies etc. are like nodes in the network, and…
❤2👍1
📄A Survey of Link Prediction Techniques
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Predictionc #Techniques #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Predictionc #Techniques #Survey
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