Starts on

Ends on

Lecturer

Konstantin Avrachenkov ,

Content and organization

This course will provide statistical tools to study and analyze complex networks.

Since interactions between agents arise in various situations, networks have an exceptional impact on both science and society. We can mention the various interactions between people (communication networks, social networks), between proteins (biological networks), between particles (statistical physics), or between economic agents (countries, companies, etc.). Many of these real networks are very large (Twitter, Facebook, etc.), and data might not always be fully available (or its access is restricted via API, like Twitter).

We will begin by introducing the classic random graphs models (Erdos-Renyi, Configuration Model, Preferential Attachment, etc.). Then, we will give some standard algorithms for community detection (Spectral Clustering, Louvain, Modularity, etc.). Last, we propose to describe and analyze methods to sample average or maximal values of network functions, such as degree, population opinion, or rating. Half of the course hours will be spent on implementing the methods in Python (using the package networkX).

Course Type

ai-phd Course

Host Institution
Université Côte d'Azur

Other short courses

11. 05. 2022 Go

Geometric learning

05. 04. 2022 Go

Computer Graphics

04. 04. 2022 Go

Bayesian Learning

02. 04. 2022 Go

Computer Graphics

31. 03. 2022 Go

Web of Data

27. 03. 2022 Go

Machine Learning

02. 03. 2022 Go

Player Modeling

28. 02. 2022 Go

Player Modeling

21. 02. 2022 Go

Affective Computing

21. 02. 2022 Go

Machine Listening

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Self-Driving Cars

21. 02. 2022 Go