graph

Data

subject:: Data Science Methods for Large Scale Graphs
parent:: Graph Signals and Graph Signal Processing
theme:: math notes

Graph

A graph is a triplet G=(V,E,W) where

  • V={1,…,n},|V|=n vertex set
  • E⊆V×V edge set
  • W:E→R weight function

Mentions

File
color refinement algorithm
convergent graph sequence
cut distance
degree matrix
feature-aware spectral embeddings
fixed coefficients yield the same spectral response for both graphon and graph convolutions
graph attention model
graph edit distance
graph isomorphism
graph laplacian
graphon
homomorphism density
integral lipschitz filters are stable to dilations
interpretation of the symmetric graph laplacian
linear graph filter
readout layer
spectral embedding
spectral representation of graphon convolutions
undirected graph
unweighted graph
ways to sample graphs from graphons
we can use GNNs to solve feature-aware semi-supervised learning problems
2025-01-22 graphs lecture 1
2025-01-27 graphs lecture 2
2025-01-29 graphs lecture 3
2025-02-10 graphs lecture 6
2025-02-12 graphs lecture 7
2025-02-19 graphs lecture 9
2025-03-03 graphs lecture 11
2025-03-10 graphs lecture 13
2025-03-24 graphs lecture 14
2025-03-26 lecture 15
2025-04-02 lecture 17
2025-04-14 lecture 20