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Enseignement scientifique & technique - SD212 : Graph Mining

Domaine > Mathématiques, Informatique.

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Descriptif

The focus of this course is on graph mining, a set of techniques for analysing and learning from graph data (e.g., social networks, Web graphs, knowledge graphs, biological networks)

You will learn how real graphs are structured, with a focus on the scale-free and small-world properties.

You will also learn how to find the most important nodes in the graph, how to detect clusters of nodes, and how to predict labels using the graph structure.

A large part of the course will be devoted to programming in Python where you will have to implement various algorithms for analysing real datasets.

Objectifs pédagogiques

You will learn to:

  • identify graph-based data
  • represent and analyse large graphs
  • select the most appropriate technique for each learning task

Pour les étudiants du diplôme Diplôme d'ingénieur

Students are supposed to have previously acquired basic knowledge in graph algorithms (search, shortest paths), probability, and Python programming.

Pour les étudiants du diplôme Echange international non diplomant

Students are supposed to have previously acquired basic knowledge in graph algorithms (search, shortest paths), probability, and Python programming.

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Data & Artificial Intelligence

Vos modalités d'acquisition :

Quiz on Moodle

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS

La note obtenue rentre dans le calcul de votre GPA.

Pour les étudiants du diplôme Diplôme d'ingénieur

Vos modalités d'acquisition :

Quiz on Moodle

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS
  • Crédit d'UE électives acquis : 2.5

La note obtenue rentre dans le calcul de votre GPA.

Pour les étudiants du diplôme Echange international non diplomant

Vos modalités d'acquisition :

Quiz on Moodle

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2.5 ECTS

La note obtenue rentre dans le calcul de votre GPA.

Programme détaillé

The program is the following:

  • Sparse matrices
  • PageRank
  • Clustering
  • Hierarchical clustering
  • Heat diffusion
  • Spectral embedding
  • Graph neural networks

Mots clés

Graphs, sparse matrices, machine learning

Méthodes pédagogiques

Lectures + Quizzes + Labs
Veuillez patienter