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🎞 Emergence of echo chambers and polarization dynamics in social networks

πŸ’₯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.

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video #tutorial
πŸ“„Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis

πŸ“˜Journal: Complexity (I.F= 2,83 )
πŸ—“Publish year: 2022

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
2021_Application_of_complex_systems_topologies_in_artificial_neural.pdf
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πŸ“„Application of complex systems topologies in artificial neural networks optimization: An overview

πŸ“˜Journal: Expert Systems with Applications (I.F= 6.954)
πŸ—“Publish year: 2021

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #biology #link_prediction
πŸ‘1
πŸ“• Social Network Data Analytics

🌐 Download the ebook

πŸ“²Channel: @ComplexNetworkAnalysis

#ebook
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πŸ“„Random complex networks

πŸ“˜Journal
: National Science Review(I.F= 16.693)

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
🎞 Order and Disorder in Network Science

πŸ’₯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.

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video #tutorial
2018_Link prediction potentials for biological networks.pdf
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πŸ“„ Link prediction potentials for biological networks

πŸ“˜ Journal: International Journal of Data Mining and Bioinformatics (I.F=0.667)

πŸ—“ Publish year: 2018

πŸ“Ž Study paper

πŸ“±Channel:
@ComplexNetworkAnalysis
#paper #linkprediction #biology
πŸ“• Network Analysis: Methodological Foundations

🌐 Download the ebook

πŸ“²Channel: @ComplexNetworkAnalysis

#ebook
🎞 Power law and scale-free networks.

πŸ’₯Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.

πŸ’₯Power law distribution. Scale-free networks.Pareto distribution, normalization, moments. Zipf law. Rank-frequency plot.

πŸ“½ Watch

πŸ“‘Lecture

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture
πŸ“„A Survey of Link Prediction in Complex Networks

πŸ—“Publish year: 2016

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Survey #linkprediction
πŸ“„Complex Networks in Manufacturing and Logistics: A Retrospect

πŸ“˜ Book: Dynamics in Logistics

πŸ—“Publish year: 2021

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
πŸ“„A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders

πŸ—“Publish year: 2021

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
πŸ‘1
Forwarded from Bioinformatics
🎬Introduction to Biological Network Analysis
πŸ‘©β€πŸ«Mini Courses from Donna Slonim at Tufts University

Session
1: Network Basics and Properties
Session 2: From Graphs to Function
Session 3: Identifying Network Modules
Session 4: Network Alignment and Querying

πŸ“²Channel: @Bioinformatics
πŸ“„ Seven-Layer Model in Complex Networks Link Prediction: A Survey

πŸ“˜Journal: Sensors (I.F=3.576)

πŸ—“Publish year: 2020

πŸ“Ž Study paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #survey #linkprediction
🎞 Introduction to Social Network Analysis [4/5]: Graph Interpretation

πŸ’₯Free recorded workshop by Martin Grandjean (UniversitΓ© de Lausanne) at the Conference HNR+ResHist2021 Conference "Historical Networks - RΓ©seaux Historiques - Historische Netzwerke co-organised by HNR and ResHist.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #workshop
πŸ“„ Network analysis of protein interaction data

πŸ’₯Free course From EMBL-EBI

πŸ’₯This course provides an introduction to the theory and concepts of network analysis. It explores some of the features of protein-protein interaction networks and their implications for biology. Finally, the course discusses the tools and strategies that can be used to build and analyse biological networks.

πŸ“ŽStudy course

πŸ“²Channel: @ComplexNetworkAnalysis

#course
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πŸ“•Gephi Cookbook

πŸ“₯ (PDF): Free download

πŸ“±Channel: @ComplexNetworkAnalysis

#book #Gephi
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🎞 Random graphs

πŸ’₯Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.

πŸ’₯Erdos-Reni random graph model. Poisson and Bernulli distributions. Distribution of node degrees. Phase transition, gigantic connected component. Diameter and cluster coefficient. Configuration model
.

πŸ“½ Watch

πŸ“‘ Lecture

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture
Complex Networks in Software, Knowledge, and Social Systems
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