2023_A_survey_on_identification_of_influential_users_in_social_media.pdf
460.6 KB
π A survey on identification of influential users in social media networks using bio inspired algorithms
πjournal: Procedia Computer Science
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #identification #influential #users #bio #inspired #algorithms
πjournal: Procedia Computer Science
πPublish year: 2023
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #identification #influential #users #bio #inspired #algorithms
π3
2017-Python for Graph and Network Analysis.pdf
13 MB
πPython for Graph and Network Analysis
πPublish year: 2017
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book #Python #Graph
πPublish year: 2017
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book #Python #Graph
π3
π Link Prediction on Complex Networks: An Experimental Survey
πjournal: Data Science and Engineering (I.F=4.52)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Survey
πjournal: Data Science and Engineering (I.F=4.52)
πPublish year: 2022
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Survey
π2
πGraph-powered learning methods in the Internet of Things: A survey
πjournal: Machine Learning with Applications (I.F=3.203)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #IOT #Survey
πjournal: Machine Learning with Applications (I.F=3.203)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #IOT #Survey
π3
From_Social_Networks_to_Time_Series_Methods_and_Applications_1.pdf
1 MB
πFrom Social Networks to Time Series: Methods and Applications
πAuthors: Tongfeng Weng, Yaofeng Zhang, Pan Hui.
π publish year: 2017
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #Social_Network #Application
πAuthors: Tongfeng Weng, Yaofeng Zhang, Pan Hui.
π publish year: 2017
π Study book
π²Channel: @ComplexNetworkAnalysis
#book #Social_Network #Application
π4
π Machine Learning with Graphs: Theory of Graph Neural Networks
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: How expensive are graph neural networks, designing the most powerful GNNs.
π½ Watch: part1 part2
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯The topics: How expensive are graph neural networks, designing the most powerful GNNs.
π½ Watch: part1 part2
π²Channel: @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 Stanfordβs 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 powerβ¦
Jure Leskovec
Computer Science, PhD
In this lecture, we provide a theoretical framework to analyze the expressive powerβ¦
π1
π Precision medicine β networks to the rescue
πjournal: Current Opinion in Biotechnology (COBIOT) (I.F=10.279)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #iPrecision #medicine #networks #rescue
πjournal: Current Opinion in Biotechnology (COBIOT) (I.F=10.279)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #iPrecision #medicine #networks #rescue
π4
π Role-Aware Information Spread in Online Social Networks
πjournal: ENTROPY-SWITZ (I.F=2.738)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Role_Aware #Information #Spread #Online
πjournal: ENTROPY-SWITZ (I.F=2.738)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Role_Aware #Information #Spread #Online
π1
πA Review of Graph and Network Complexity from an Algorithmic Information Perspective
πjournal: Entropy (I.F=2.738)
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
πjournal: Entropy (I.F=2.738)
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
π5π1
π A survey of community detection methods in multilayer networks
πjournal: Data Mining and Knowledge Discovery (I.F=5.406)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #community_detection #methods #multilayer #survey
πjournal: Data Mining and Knowledge Discovery (I.F=5.406)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #community_detection #methods #multilayer #survey
π1
πConsidering weights in real social networks: A review
πjournal: Frontiers in Physics(I.F=3.718)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_networks #Review
πjournal: Frontiers in Physics(I.F=3.718)
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #social_networks #Review
π2π1
π Community detection for multilayer weighted networks
πjournal: Information Sciences(I.F=8.233)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Community_detection #multilayer #weighted_networks
πjournal: Information Sciences(I.F=8.233)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Community_detection #multilayer #weighted_networks
π2
πRecent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #machine_learning #Application
πPublish year: 2023
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #machine_learning #Application
π4
π Network Analysis of Organizations
π₯Free recorded course by professor Daniel A. McFarland.
π₯In this course, we will describe how organizationβs 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.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #network
π₯Free recorded course by professor Daniel A. McFarland.
π₯In this course, we will describe how organizationβs 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.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #network
π2
π Review on Learning and Extracting Graph Features for Link Prediction
πjournal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Learning #Extracting #Graph #Features #Link_Prediction #review
πjournal: MACHINE LEARNING AND KNOWLEDGE EXTRACTION
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Learning #Extracting #Graph #Features #Link_Prediction #review
π1
π Network Theory
π₯Free recorded course.
π₯This lecture will discuss Network Theory:
Part I β Static networks:
πΈUnderstand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
πΈUnderstand different topologies and how they affect the network:
-Random
-Preferential
πΈKnow the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
πΈUnderstand basic network evolution processes:
-Small world networks
Part II β Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
πΈNetworks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
πΈMeasuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
πΈProcesses on networks:
-Avalanche models
-Metcalfeβs law
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
π₯Free recorded course.
π₯This lecture will discuss Network Theory:
Part I β Static networks:
πΈUnderstand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
πΈUnderstand different topologies and how they affect the network:
-Random
-Preferential
πΈKnow the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
πΈUnderstand basic network evolution processes:
-Small world networks
Part II β Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
πΈNetworks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
πΈMeasuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
πΈProcesses on networks:
-Avalanche models
-Metcalfeβs law
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
TU Delft OCW
Network Theory - TU Delft OCW
π3
π A survey of graph neural network based recommendation in social networks
πjournal: Neurocomputing(I.F=5.779)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #graph_neural_network #Extracting #recommendation #survey
πjournal: Neurocomputing(I.F=5.779)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #graph_neural_network #Extracting #recommendation #survey
π1
π Critical Review of Social Network Analysis Applications in Complex Project Management
πjournal: Journal of Management in Engineerin(I.F=6.415)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Critical #Applications #Complex_Project #Management #Review
πjournal: Journal of Management in Engineerin(I.F=6.415)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Critical #Applications #Complex_Project #Management #Review
π2
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