📄 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,…
👍4👏1
📄 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
👍5
📄Structure and function of complex brain networks
📘Journal: Dialogues in Clinical Neuroscience (DCNS)
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Structure #function #complex #brain #networks
📘Journal: Dialogues in Clinical Neuroscience (DCNS)
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Structure #function #complex #brain #networks
📑A Survey on Studying the Social Networks of Students
🗓Publish year: 2019
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Survey
🗓Publish year: 2019
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Survey
👍2
🎞 Social Network Analysis Methods for Network Building and Impact
💥Free recorded session about social network analysis method
💥This session will discuss the social network analysis methodology and how it can be applied and leveraged in fellowships and with alumni networks for multiple benefits.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Methods #Network_Building #Impact
💥Free recorded session about social network analysis method
💥This session will discuss the social network analysis methodology and how it can be applied and leveraged in fellowships and with alumni networks for multiple benefits.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Methods #Network_Building #Impact
YouTube
Social Network Analysis Methods for Network Building and Impact
This session will discuss the social network analysis methodology and how it can be applied and leveraged in fellowships and with alumni networks for multiple benefits.
👍2
📄Centrality measures in fuzzy social networks
📘journal: Information systems (l.F=7.767)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network #fuzzy #Centrality
📘journal: Information systems (l.F=7.767)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network #fuzzy #Centrality
2020_Applications_of_link_prediction_in_social_networks_A_review.pdf
2.4 MB
📄Applications of link prediction in social networks: A review
📘journal: Journal of Network and Computer Applications (I.F=8.7)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Applications #link_prediction #review
📘journal: Journal of Network and Computer Applications (I.F=8.7)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Applications #link_prediction #review
📄Influence maximization on temporal
networks: a review
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #temporal_networks #review
networks: a review
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #temporal_networks #review
2020_A_survey_on_network_node_ranking_algorithms_Representative.pdf
793.4 KB
📄A survey on network node ranking algorithms: Representative methods, extensions, and applications
📘journal: Science China Technological Sciences(I.F= 4.6)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #network #node #ranking #algorithms #Representative #methods #extensions #applications #survey
📘journal: Science China Technological Sciences(I.F= 4.6)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #network #node #ranking #algorithms #Representative #methods #extensions #applications #survey
❤2
📄Python modularity Examples
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
📄Network Analysis Based on Important Node Selection and Community Detection
📘journal: MATHEMATICS-BASEL(I.F= 2.4)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
📘journal: MATHEMATICS-BASEL(I.F= 2.4)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
📄Community Detection
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
👍1