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2018_Study_on_centrality_measures_in_social_networks_a_survey.pdf
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πŸ“„Study on centrality measures in social networks: a survey

πŸ“˜Journal: Social Network Analysis and Mining (I.F=3.868)

πŸ—“Publish year: 2018

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πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #survey #centrality
πŸ“„A Critical Review of Centrality Measures in Social Networks

πŸ“˜Journal: Business & Information Systems Engineering (I.F=4.532)

πŸ—“Publish year: 2010

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πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Review #Centrality
πŸ“‘Considering weights in real social networks: A review

πŸ“˜journal: Frontiers in Physics(I.F=3.718)
πŸ—“Publish year: 2023

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πŸ“²Channel: @ComplexNetworkAnalysis
#paper #social_networks #Review
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πŸ“‘A Survey on Studying the Social Networks of Students

πŸ—“Publish year: 2019

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#paper #Social_Networks #Survey
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πŸ“š Knowledge Graphs

✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.

πŸ§‘β€πŸ’Ό authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
πŸ—“Publish year: 2021

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πŸ“²Channel: @ComplexNetworkAnalysis
#Book #graph #Data_Graphs #Graph_Algorithms #Graph_Analytics #Graph_Neural_Networks #Knowledge_Graphs #Social_Networks
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