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๐Ÿ“ƒNetwork analysis of protein interaction data: an introduction

๐Ÿ’ฅGood introductory document from EBI

๐ŸŒ Study the tutorial

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#tutorial
๐ŸŽž Emergence of echo chambers and polarization dynamics in social networks

๐Ÿ’ฅEcho 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.

๐Ÿ“ฝ Watch

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis

#video #tutorial
๐ŸŽž Order and Disorder in Network Science

๐Ÿ’ฅA 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.

๐Ÿ“ฝ Watch

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis

#video #tutorial
๐Ÿ“„Machine Learning in Network Centrality Measures: Tutorial and Outlook

๐Ÿ“˜Journal: ACM Computing Surveys (I.F=10.282)

๐Ÿ—“Publish year: 2019

๐Ÿ“Ž Study paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #centrality
๐Ÿ“„A tutorial on modeling and analysis of dynamic social networks. Part I

๐Ÿ“˜
Journal: Annual Reviews in Control (I.F=10.699)

๐Ÿ—“Publish year: 2017

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #tutorial #dynamic
๐ŸŽžGEPHI โ€“ Introduction to Network Analysis and Visualization

๐Ÿ’ฅFree online tutorial and recorded course

๐Ÿ’ฅNetwork Analysis and visualization appears to be an interesting tool to give the researcher the ability to see its data from a new angle. Because Gephi is an easy access and powerful network analysis tool, we propose a tutorial designed to allow everyone to make his first experiments on two complementary datasets.

๐Ÿ“ฝ Watch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #Gephi #tutorial
๐Ÿ‘1
๐ŸŽž Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..

๐Ÿ’ฅFree recorded tutorial by Avkash Chauhan.

๐Ÿ’ฅThis
tutorial is part one of a two parts GNN series. Graphs helps us understand and visualize the relationship and connection information in a natural and close to human behavior. Graph Neural networks are solving various machine learning problems where CNN or convolutional neural networks can not be applied. Then You will learn GNN technical details along with hands on exercise using Python programming along with NetworkX, PyG (pytorch_geometric) , matplotlib libraries.

๐Ÿ“ฝ Watch: part1 part2

๐Ÿ’ป Code

๐Ÿ“œ Slides

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #tutorial #Graph #GNN #Python #NetworkX #PyG
๐ŸŽž A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

๐Ÿ’ฅFree recorded tutorial by Andre M. Bastos
๐Ÿ”นThis
tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, I will review metrics for functional connectivity, including coherence, phase synchronization, phase slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition Next, I will highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a series of MATLAB-scripts, which can be executed by the tutorial participants to simulate each of these potential problems. I will discuss how some of these issues can be addressed using current methods

๐Ÿ“ฝWatch

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis

#video #Tutorial #Connectivity #review