2020_Interactively_Visualize_and_Analyze_Social_Network_Gephi.pdf
2.4 MB
📄Interactively Visualize and Analyze Social Network Gephi
📘Conference : 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Gephi #Social_Network
📘Conference : 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Gephi #Social_Network
👍1
🎞 Machine Learning with Graphs: Node Embeddings
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥Traditional Feature-based Methods: Node-level features ,Link-level features ,Graph-level features
📽 Watch: part1 part2 part3
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥Traditional Feature-based Methods: Node-level features ,Link-level features ,Graph-level features
📽 Watch: part1 part2 part3
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.1 - Node Embeddings
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cv1BEU
Jure Leskovec
Computer Science, PhD
From previous lectures we see how we can use machine learning with feature engineering…
Jure Leskovec
Computer Science, PhD
From previous lectures we see how we can use machine learning with feature engineering…
📄Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Anomaly_Detection #Challenges
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Anomaly_Detection #Challenges
📄Social Network Analysis Visualization Tools: A Comparative Review
📘Conference: 2020 IEEE 23rd International Multitopic Conference (INMIC)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network #Review #Tools
📘Conference: 2020 IEEE 23rd International Multitopic Conference (INMIC)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network #Review #Tools
2018_Social_network_analysis_Characteristics_of_online_social_networks.pdf
1.8 MB
📄Social network analysis: Characteristics of online social networks after a disaster
📘Journal: International Journal of Information Management (I.F=14.098)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📘Journal: International Journal of Information Management (I.F=14.098)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
2017-Efficiency assessment using network analysis tools.pdf
2.7 MB
📄Efficiency assessment using network analysis tools
📘Journal: Journal of the Operational Research Society (I.F=2.175)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #tools
📘Journal: Journal of the Operational Research Society (I.F=2.175)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #tools
2020_Social_network_analysis_organizational_implications_in_tourism.pdf
146.9 KB
📄Social network analysis: organizational implications in tourism management
📘Journal: Social network
analysis (I.F=4.144)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📘Journal: Social network
analysis (I.F=4.144)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
2020_Italian_tourism_intermediaries_a_social_network_analysis_exploration.pdf
1.8 MB
📄Italian tourism intermediaries: a social network analysis exploration
📘Journal: Current Issues in Tourism
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📘Journal: Current Issues in Tourism
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
Social network analysis A methodological introduction.pdf
330 KB
📄Social network analysis: A methodological introduction
📘Journal: Asian Journal of Social Psychology (I.F= 1.424)
💥Abstract: Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individual-level covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📘Journal: Asian Journal of Social Psychology (I.F= 1.424)
💥Abstract: Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individual-level covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
👍2
📄Applying Social Network Analysis to Identify Project Critical Success Factors
📘Journal: Sustainability (I.F= 3.889)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📘Journal: Sustainability (I.F= 3.889)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_network
📄Network analysis of international export pattern
📘Journal: Social Network Analysis and Mining (SNAM)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #export
📘Journal: Social Network Analysis and Mining (SNAM)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #export
👍1
🎞 Complex Network for Data Analysis. Stefano Boccaletti
💥Free recorded lecture by Stefano Boccaletti
💥In this fourth lecture, I will describe how networks can be used for data analysis, and will in particular introduce two classes of networks (functional networks and parenclitic networks) which have recently found application in various fields of science
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video
💥Free recorded lecture by Stefano Boccaletti
💥In this fourth lecture, I will describe how networks can be used for data analysis, and will in particular introduce two classes of networks (functional networks and parenclitic networks) which have recently found application in various fields of science
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video
YouTube
Complex Network for Data Analysis. Stefano Boccaletti
Audio is missing until 24:45
Course playlist: https://www.youtube.com/playlist?list=PL4_hYwCyhAvYRMcCHSVbghzmpa8gOtli4
Date: 19.11.2019
In this fourth lecture, I will describe how networks can be used for data analysis, and will in particular introduce…
Course playlist: https://www.youtube.com/playlist?list=PL4_hYwCyhAvYRMcCHSVbghzmpa8gOtli4
Date: 19.11.2019
In this fourth lecture, I will describe how networks can be used for data analysis, and will in particular introduce…
Forwarded from Bioinformatics
📑Challenges and opportunities in network-based solutions for biological questions
📘Journal: Briefing in Bioinformatics (I.F.=13.994)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @Bioinformatics
#network #challenges
📘Journal: Briefing in Bioinformatics (I.F.=13.994)
🗓Publish year: 2022
📎 Study the paper
📲Channel: @Bioinformatics
#network #challenges
📄Time Series Forecasting Based on Complex Network Analysis
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series
📄Scikit-network: Graph Analysis in Python
📘Journal: Journal of Machine Learning Research (I.F= 4.091)
🗓Publish year: 2020
💥Abstract: Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #python #tools
📘Journal: Journal of Machine Learning Research (I.F= 4.091)
🗓Publish year: 2020
💥Abstract: Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #python #tools
Social Network Analysis and Mining for Business Applications.pdf
285.1 KB
📄Social Network Analysis and Mining for Business Applications
📘Journal: Network Analysis and Mining for Business Applications (I.F= 3.868)
🗓Publish year: 2011
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Business
📘Journal: Network Analysis and Mining for Business Applications (I.F= 3.868)
🗓Publish year: 2011
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Business
👍3
📄Graph-based network analysis of resting-state functional MRI
📘Journal: Frontiers in Systems Neuroscience (I.F= 3.203)
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #brain
📘Journal: Frontiers in Systems Neuroscience (I.F= 3.203)
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #brain
👍1
2010_Social_network_analysis_developments,_advances,_and_prospects.pdf
132.5 KB
📄Social network analysis: developments, advances, and prospects
📘Journal:Social Network Analysis and Mining (I.F= 4.229)
🗓Publish year: 2010
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network
📘Journal:Social Network Analysis and Mining (I.F= 4.229)
🗓Publish year: 2010
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network
Forwarded from Bioinformatics
📃Graph Models for Biological Pathway Visualization: State of the Art and Future Challenges
🗓Publish year: 2021
📎 Study the paper
📲Channel: @Bioinformatics
#pathway #visualization
🗓Publish year: 2021
📎 Study the paper
📲Channel: @Bioinformatics
#pathway #visualization
📄Using Names Lists for Social Network Analysis
📘Journal:Umanistica Digitale
🗓Publish year: 2019
💥Abstract: In this paper, I discuss using digital names lists compiled from analog sources for social network analysis. Using examples from finding aids of archival collections, I demonstrate how social network analysis can show relationships and contrasts between different datasets of names or be used to show relationships within a single set of names. Data visualization tools such as Gephi aid in the analysis and present the relationships in an easily understandable way. Exposing relationships between collections or within a collection increases its findability.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Gephi
📘Journal:Umanistica Digitale
🗓Publish year: 2019
💥Abstract: In this paper, I discuss using digital names lists compiled from analog sources for social network analysis. Using examples from finding aids of archival collections, I demonstrate how social network analysis can show relationships and contrasts between different datasets of names or be used to show relationships within a single set of names. Data visualization tools such as Gephi aid in the analysis and present the relationships in an easily understandable way. Exposing relationships between collections or within a collection increases its findability.
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Social_Network #Gephi
👍3