2022_A_Review_on_Opinion_Leader_Detection_and_its_Applications.pdf
630.7 KB
📄A Review on Opinion Leader Detection and its Applications
📘Conference: International Conference on Communication and Electronics Systems (ICCES)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Opinion #Leader #paper #Detection #Applications #Review
📘Conference: International Conference on Communication and Electronics Systems (ICCES)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Opinion #Leader #paper #Detection #Applications #Review
📄 20 years of network community detection
📘journal: NATURE PHYSICS(I.F=19.684)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #years #community_detection
📘journal: NATURE PHYSICS(I.F=19.684)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #years #community_detection
2023_A_survey_on_identification_of_influential_users_in_social_media.pdf
460.6 KB
📄 A survey on identification of influential users in social media networks using bio inspired algorithms
📘journal: Procedia Computer Science
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #identification #influential #users #bio #inspired #algorithms
📘journal: Procedia Computer Science
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #identification #influential #users #bio #inspired #algorithms
2017-Python for Graph and Network Analysis.pdf
13 MB
📕Python for Graph and Network Analysis
🗓Publish year: 2017
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Python #Graph
🗓Publish year: 2017
📎 Study the book
📱Channel: @ComplexNetworkAnalysis
#book #Python #Graph
📑 Link Prediction on Complex Networks: An Experimental Survey
📘journal: Data Science and Engineering (I.F=4.52)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Survey
📘journal: Data Science and Engineering (I.F=4.52)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Survey
📑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
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
🎞 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…
📄 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
📄 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
📑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
📄 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
📑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
📄 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
📑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
🎞 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
📄 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
🎞 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
📄 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