Lecture

Social and Information Networks: Processes

In course

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Description

This lecture covers computing measures over large networks, sampling, random walks on graphs, handling degree and locality bias, conductance bound, epidemics, unbiased estimators from random walks, transient analysis, mixing time, and epidemic models in social and information networks.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_0nnnlowd

About this result

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Mathematics

Discrete mathematics: Graph theory

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