Graph Mining: Link Based Ranking and Document Classification

In course

Magna dolor culpa eu eiusmod. Nisi magna in incididunt labore proident dolor fugiat non qui esse officia excepteur est. Aliqua cillum veniam consequat velit culpa laboris dolore exercitation et aute laboris nulla sunt commodo. Fugiat eiusmod commodo qui ex eu. Tempor excepteur cillum velit aliquip pariatur magna mollit amet aute cupidatat. Ad esse qui minim adipisicing deserunt non. Pariatur sint reprehenderit dolor nostrud deserunt.

Description

This lecture covers topics related to graph mining, including link-based ranking and document classification. It discusses techniques such as exploiting locality in link-based ranking, convergence of HITS algorithm, and computing betweenness in social graphs. The instructor also explains the concept of varying length encoding and the use of gap encoding in inverted files.

Instructor

nulla pariatur voluptate do

In eiusmod occaecat dolore voluptate pariatur cillum sunt do voluptate qui veniam dolore. Nostrud anim aliquip nostrud veniam laboris dolore tempor nulla amet officia aliquip aliqua. Amet nisi ea do ex esse. Nulla sit sunt dolor proident cupidatat elit. Pariatur ipsum elit ea in ipsum est culpa incididunt dolore.

Official source

https://mediaspace.epfl.ch/media/0_6chwujty

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (33)