📄Network data
💥This page contains links to some network data sets I've compiled over the years. All of these are free for scientific use to the best of my knowledge, meaning that the original authors have already made the data freely available, or that I have consulted the authors and received permission to the post the data here, or that the data are mine. If you make use of any of these data, please cite the original sources.
📎Most popular network datasets
📲Channel: @ComplexNetworkAnalysis
#dataset
💥This page contains links to some network data sets I've compiled over the years. All of these are free for scientific use to the best of my knowledge, meaning that the original authors have already made the data freely available, or that I have consulted the authors and received permission to the post the data here, or that the data are mine. If you make use of any of these data, please cite the original sources.
📎Most popular network datasets
📲Channel: @ComplexNetworkAnalysis
#dataset
👍1
📄Pearson Correlations on Complex Networks
📘Journal: Journal of Complex Networks (I.F= 2.011)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: Journal of Complex Networks (I.F= 2.011)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📄A Survey on the Role of Centrality as Seed Nodes for Information Propagation in Large Scale Network
📘Journal: ACM/IMS Transactions on Data Science
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey
📘Journal: ACM/IMS Transactions on Data Science
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey
👍1
📄Local controllability of complex networks
📘Journal: Mathematical Modelling and Control (I.F= 5.129)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: Mathematical Modelling and Control (I.F= 5.129)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📄A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
💥 Technical paper
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
💥 Technical paper
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
🎞 Complex Network: Theory and Application
💥Free recorded course by Prof. Animesh Mukherjee, Department of Computer Science and Engineering, IIT Kharagpur.
💥This course covers lessons in network analysis, properties of social networks, community analysis, and case study of citation networks. Study of the models and behaviors of networked systems. Empirical studies of social, biological, technological and information networks. Exploring the concepts of small world effect, degree distribution, clustering, network correlations, node centrality, and community structure of networks. This will be followed by detailed case study of citation networks. Types of network: Social networks, Information networks, Technological networks, Biological networks, Citation Networks. Properties of network: Small world effect, transitivity and clustering, degree distribution, scale free networks, maximum degree; mixing patterns; degree correlations; community structures; node centrality.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course
💥Free recorded course by Prof. Animesh Mukherjee, Department of Computer Science and Engineering, IIT Kharagpur.
💥This course covers lessons in network analysis, properties of social networks, community analysis, and case study of citation networks. Study of the models and behaviors of networked systems. Empirical studies of social, biological, technological and information networks. Exploring the concepts of small world effect, degree distribution, clustering, network correlations, node centrality, and community structure of networks. This will be followed by detailed case study of citation networks. Types of network: Social networks, Information networks, Technological networks, Biological networks, Citation Networks. Properties of network: Small world effect, transitivity and clustering, degree distribution, scale free networks, maximum degree; mixing patterns; degree correlations; community structures; node centrality.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course
Infocobuild
Complex Network: Theory and Application (Prof. Animesh Mukherjee, IIT Kharagpur): Lecture 08 - Social Network Principles I: As…
Complex Network: Theory and Application (Prof. Animesh Mukherjee, IIT Kharagpur): Lecture 08 - Social Network Principles I: Assortativity/Homophily, Signed Graphs.
🎞 Social Network Analysis
💥This free recorded tutorial is an overview of social networks and social network analysis.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥This free recorded tutorial is an overview of social networks and social network analysis.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Social Network Analysis
An overview of social networks and social network analysis.
See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
🎞 Introduction to Social Network Analysis [3/5]: Historical Applications
💥Free recorded workshop by Martin Grandjean (Université de Lausanne) at the Conference HNR+ResHist2021 Conference "Historical Networks - Réseaux Historiques - Historische Netzwerke co-organised by HNR and ResHist.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #workshop
💥Free recorded workshop by Martin Grandjean (Université de Lausanne) at the Conference HNR+ResHist2021 Conference "Historical Networks - Réseaux Historiques - Historische Netzwerke co-organised by HNR and ResHist.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #workshop
YouTube
Introduction to Social Network Analysis [3/5]: Historical Applications
Workshop by Martin Grandjean (Université de Lausanne) at the Conference HNR+ResHist2021 Conference "Historical Networks - Réseaux Historiques - Historische Netzwerke co-organised by HNR and ResHist.
The noscript is available here: https://doi.org/10.5281/zenodo.5083036…
The noscript is available here: https://doi.org/10.5281/zenodo.5083036…
👏1
2020_Review_on_Social_Network_Trust_With_Respect_To_Big_Data_Analytics.pdf
328.4 KB
📄Review on Social Network Trust With Respect To Big Data Analytics
📘Conference: Fourth International Conference on Trends in Electronics and Informatics (ICOEI 2020)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #review
📘Conference: Fourth International Conference on Trends in Electronics and Informatics (ICOEI 2020)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #review
👍1
🎞 Gephi Tutorial on Network Visualization and Analysis
💥This free recorded tutorial goes from import through the whole analysis phase for a citation network.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial #gephi
💥This free recorded tutorial goes from import through the whole analysis phase for a citation network.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial #gephi
YouTube
Gephi Tutorial on Network Visualization and Analysis
This tutorial goes from import through the whole analysis phase for a citation network. Data can be accessed at http://www.cs.umd.edu/~golbeck/INST633o/Viz.shtml
📄Centralities in complex networks
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
🎞 Emergence of echo chambers and polarization dynamics in social networks
💥Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. In this talk, we will first review empirical evidence for the presence of echo chambers across social media platforms, by performing a comparative analysis among Gab, Facebook, Reddit, and Twitter. Then, we will present a simple modeling framework able to reproduce the observed opinion segregation in the social network. We consider networked agents characterized by heterogeneous activities and homophily, whose opinions can be reinforced by interactions with like-minded peers. We show that the transition between a global consensus and emerging polarized states in the network can be analytically characterized as a function of the social influence of the agents and the controversialness of the topic discussed. Finally, we consider a generalization to multiple opinions with respect to different topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we frame this problem in a formalism in which opinions evolve in a multidimensional space where topics form a non-orthogonal basis. We show that this approach can reproduce the correlations between extreme opinions on different topics found in survey data.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena stay unclear. In this talk, we will first review empirical evidence for the presence of echo chambers across social media platforms, by performing a comparative analysis among Gab, Facebook, Reddit, and Twitter. Then, we will present a simple modeling framework able to reproduce the observed opinion segregation in the social network. We consider networked agents characterized by heterogeneous activities and homophily, whose opinions can be reinforced by interactions with like-minded peers. We show that the transition between a global consensus and emerging polarized states in the network can be analytically characterized as a function of the social influence of the agents and the controversialness of the topic discussed. Finally, we consider a generalization to multiple opinions with respect to different topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we frame this problem in a formalism in which opinions evolve in a multidimensional space where topics form a non-orthogonal basis. We show that this approach can reproduce the correlations between extreme opinions on different topics found in survey data.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Emergence of echo chambers and polarization dynamics in social networks - Michele Starnini
Emergence of echo chambers and polarization dynamics in social networks
Abstract: Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on…
Abstract: Echo chambers and opinion polarization, recently quantified in several sociopolitical contexts and across different social media, raise concerns on their potential impact on…
📄Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis
📘Journal: Complexity (I.F= 2,83 )
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: Complexity (I.F= 2,83 )
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
2021_Application_of_complex_systems_topologies_in_artificial_neural.pdf
860.1 KB
📄Application of complex systems topologies in artificial neural networks optimization: An overview
📘Journal: Expert Systems with Applications (I.F= 6.954)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #biology #link_prediction
📘Journal: Expert Systems with Applications (I.F= 6.954)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #biology #link_prediction
👍1
📄Random complex networks
📘Journal: National Science Review(I.F= 16.693)
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: National Science Review(I.F= 16.693)
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
🎞 Order and Disorder in Network Science
💥A recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopic scale. Reversely, scientists have been fascinated by the emergence of spatial and temporal patterns in interacting systems. In this talk, I will give a personal view on these two sides within the field of network science, whose combination of order and randomness is at the core of several works on network dynamics and algorithms.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
💥A recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopic scale. Reversely, scientists have been fascinated by the emergence of spatial and temporal patterns in interacting systems. In this talk, I will give a personal view on these two sides within the field of network science, whose combination of order and randomness is at the core of several works on network dynamics and algorithms.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #tutorial
YouTube
Order and Disorder in Network Science - Renaud Lambiotte
A recurring theme in the study of complex systems is the emergence of order and disorder in systems. Historically, one can think of the Boltzmann equation, and the irreversible growth of disorder at the macroscopic scale from reversible dynamics at the microscopic…
2018_Link prediction potentials for biological networks.pdf
407.2 KB
📄 Link prediction potentials for biological networks
📘 Journal: International Journal of Data Mining and Bioinformatics (I.F=0.667)
🗓 Publish year: 2018
📎 Study paper
📱Channel:
@ComplexNetworkAnalysis
#paper #linkprediction #biology
📘 Journal: International Journal of Data Mining and Bioinformatics (I.F=0.667)
🗓 Publish year: 2018
📎 Study paper
📱Channel:
@ComplexNetworkAnalysis
#paper #linkprediction #biology