Video Lectures
Lecture 35: Finding Clusters in Graphs
Description
The topic of this lecture is clustering for graphs, meaning finding sets of “related” vertices in graphs. The challenge is finding good algorithms to optimize cluster quality. Professor Strang reviews some possibilities.
Summary
Two ways to separate graph nodes into clusters
- k-means: Choose clusters, choose centroids, choose clusters, …
- Fiedler vector: Eigenvector of graph Laplacian: \(+-\) signs give 2 clusters
Related sections in textbook: IV.6–IV.7
Instructor: Prof. Gilbert Strang
Problem for Lecture 35
From textbook Sections IV.6-IV.7
1. What are the Laplacian matrices for a triangle graph and a square graph?
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2018
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