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
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📱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
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📄Python modularity Examples
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #modularity
💥Technical paper
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📲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
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📱Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
📘journal: MATHEMATICS-BASEL(I.F= 2.4)
🗓Publish year: 2021
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📱Channel: @ComplexNetworkAnalysis
#paper #Important_Nodes #Community_Detection
📄Community Detection
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
💥Technical paper
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #Community_Detection
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📄A Mini Review of Node Centrality Metrics in Biological Networks
📘journal: International Journal of Network Dynamics and Intelligence (IJNDI)
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #Node_Centrality #Metrics #Biological_Network #Mini_Review
📘journal: International Journal of Network Dynamics and Intelligence (IJNDI)
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #Node_Centrality #Metrics #Biological_Network #Mini_Review
📄Identifying spreading influence nodes for social networks
📘journal: Frontiers of Engineering Management (FEM)(I.F=7.4)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
📘journal: Frontiers of Engineering Management (FEM)(I.F=7.4)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
📄A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
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🎞 Social Network Analysis - network structure
💥Free recorded lecture from UCCSS (University of California Computational Social Sciences)
🔹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 the open Online Specialization link.
📽 Watch
💻Open Online Specialization
📱Channel: @ComplexNetworkAnalysis
#video #network_structure
💥Free recorded lecture from UCCSS (University of California Computational Social Sciences)
🔹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 the open Online Specialization link.
📽 Watch
💻Open Online Specialization
📱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 the…
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📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
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📄Fairness-Aware Graph Neural Networks: A Survey
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Fairness #Graph_Neural_Networks #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fairness #Graph_Neural_Networks #survey
🎞 Machine Learning with Graphs: Reasoning in Knowledge Graphs, Answering Predictive Queries, Query2box: Reasoning over KGs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 - Reasoning in Knowledge Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BweHQZ
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
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📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
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🎓Graph entropy and related topics
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
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📄Everything is Connected: Graph Neural Networks
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
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📄Network Analysis of Time Series: Novel Approaches to Network Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📄A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📄A Comprehensive Survey on Graph Neural Networks
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
📄Summary of Static Graph Embedding Algorithms
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📄Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
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