πExploring social-emotional learning, school climate, and social network analysis
πjournal: Journal of Community Psychology(I.F=2.297)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_network
πjournal: Journal of Community Psychology(I.F=2.297)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_network
π2
π Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯ 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.
π½ Watch: part1 part2
π slide
π» Code
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯ 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.
π½ Watch: part1 part2
π slide
π» Code
π²Channel: @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 Stanfordβs 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,β¦
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,β¦
π4π1
π Bibliometric review of ecological network analysis: 2010β2016
πjournal: ECOLOGICAL MODELLING(I.F=3.512)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #ecological #review
πjournal: ECOLOGICAL MODELLING(I.F=3.512)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #ecological #review
πClustering and link prediction for mesoscopic COVID-19 transmission networks in Republic of Korea
πjournal: ChaosI.F=3.436)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #link_prediction #Clustering
πjournal: ChaosI.F=3.436)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #COVID_19 #link_prediction #Clustering
π3
πDegree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network
πConference: Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
π₯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 nodesβ 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.
πPublish year: 2017
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Centrality #Social_Network #Clustering
πConference: Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
π₯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 nodesβ 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.
πPublish year: 2017
π Study the paper
π²Channel: @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, andβ¦
β€2π1
πA Survey of Link Prediction Techniques
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Predictionc #Techniques #Survey
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Link_Predictionc #Techniques #Survey
π5
πStructure and function of complex brain networks
πJournal: Dialogues in Clinical Neuroscience (DCNS)
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Structure #function #complex #brain #networks
πJournal: Dialogues in Clinical Neuroscience (DCNS)
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Structure #function #complex #brain #networks
πA Survey on Studying the Social Networks of Students
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Survey
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Survey
π2
π Social Network Analysis Methods for Network Building and Impact
π₯Free recorded session about social network analysis method
π₯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.
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #Methods #Network_Building #Impact
π₯Free recorded session about social network analysis method
π₯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.
π½ Watch
π±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.
π2
πCentrality measures in fuzzy social networks
πjournal: Information systems (l.F=7.767)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_network #fuzzy #Centrality
πjournal: Information systems (l.F=7.767)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_network #fuzzy #Centrality
2020_Applications_of_link_prediction_in_social_networks_A_review.pdf
2.4 MB
πApplications of link prediction in social networks: A review
πjournal: Journal of Network and Computer Applications (I.F=8.7)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #link_prediction #review
πjournal: Journal of Network and Computer Applications (I.F=8.7)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #link_prediction #review
πInfluence maximization on temporal
networks: a review
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #temporal_networks #review
networks: a review
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #temporal_networks #review
2020_A_survey_on_network_node_ranking_algorithms_Representative.pdf
793.4 KB
πA survey on network node ranking algorithms: Representative methods, extensions, and applications
πjournal: Science China Technological Sciences(I.F= 4.6)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #network #node #ranking #algorithms #Representative #methods #extensions #applications #survey
πjournal: Science China Technological Sciences(I.F= 4.6)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #network #node #ranking #algorithms #Representative #methods #extensions #applications #survey
β€2
πPython modularity Examples
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
πNetwork Analysis Based on Important Node Selection and Community Detection
πjournal: MATHEMATICS-BASEL(I.F= 2.4)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
πjournal: MATHEMATICS-BASEL(I.F= 2.4)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
πCommunity Detection
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
π1
πA Mini Review of Node Centrality Metrics in Biological Networks
πjournal: International Journal of Network Dynamics and Intelligence (IJNDI)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Node_Centrality #Metrics #Biological_Network #Mini_Review
πjournal: International Journal of Network Dynamics and Intelligence (IJNDI)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Node_Centrality #Metrics #Biological_Network #Mini_Review
πIdentifying spreading influence nodes for social networks
πjournal: Frontiers of Engineering Management (FEM)(I.F=7.4)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
πjournal: Frontiers of Engineering Management (FEM)(I.F=7.4)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
πA Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
π2π1
π Social Network Analysis - network structure
π₯Free recorded lecture from UCCSS (University of California Computational Social Sciences)
πΉThis lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see the open Online Specialization link.
π½ Watch
π»Open Online Specialization
π±Channel: @ComplexNetworkAnalysis
#video #network_structure
π₯Free recorded lecture from UCCSS (University of California Computational Social Sciences)
πΉThis lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see the open Online Specialization link.
π½ Watch
π»Open Online Specialization
π±Channel: @ComplexNetworkAnalysis
#video #network_structure
YouTube
UCCSS Hilbert SNA1: Social Network Analysis - network structure
This lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see theβ¦
π4
πConstruction of Knowledge Graphs: Current State and Challenges
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
π2