Syllabus

LEARNING OUTCOMES

A student who successfully completes the course will be able to: compute combinatorial quantities related to graphs, define and use centrality measures to rank nodes in a graph, know basic spectral properties of adjacency matrices, apply matrix theoretical tools to clustering problems for a network.

Credits: 5

Schedule: 23.10.2023 - 30.11.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Vanni Noferini

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

• valid for whole curriculum period:

  Review of basic graph theory, spectral theory for adjacency matrix and graph Laplacian, centrality measures, deformed graph Laplacian, spectral clustering.

Assessment Methods and Criteria

• valid for whole curriculum period:

  Project+presentation

Workload

• valid for whole curriculum period:

  19 hours lectures, 5 hours exercises, 120 hours individual study

DETAILS

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Teaching Language : English

  Teaching Period : 2022-2023 No teaching
  2023-2024 Autumn II

  Enrollment :

  Registration takes place in Sisu (sisu.aalto.fi).

Description