graph signals

Data
Graph Signals

Graph signals are data that exist on a graph G. Data are represented as vectors xRn where xi is the signal value at node i. This is often implicit, but sometimes we will use the notation (G,x).

There are two types of signals:

  • fixed node properties or features are information associated with nodes
    • ex. Nodes belong to group A or group B
  • graph signals (which often implies variability) can be interpreted as variables on the nodes of the graph
    • ex. traffic counts on the roads of minnesota

Mentions

File
1-1 vertex correspondence and 1st order changepoint localization in times series of graphs
2025-01-22 graphs lecture 1
2025-01-27 graphs lecture 2
2025-01-29 graphs lecture 3
2025-02-03 graphs lecture 4
2025-02-05 graphs lecture 5
2025-02-12 graphs lecture 7
2025-02-19 graphs lecture 9
2025-03-05 graphs lecture 12
2025-03-26 lecture 15
2025-03-31 lecture 16
2025-04-02 lecture 17
2025-04-07 lecture 18
conditions for finding a convolutional graph filter
contextual stochastic block model
convergent sequence of graph signals
convolutional graph filters are permutation equivariant
feature-aware spectral embeddings
fixed coefficients yield the same spectral response for both graphon and graph convolutions
graph automorphism
graph convolution
graph fourier transform
graph laplacian
graph signal processing problem
graphon signal
induced graphon signal
information theoretic threshold
interpretation of the graph laplacian
interpretation of the symmetric graph laplacian
inverse graph fourier transform
linear graph filter
lipschitz graph convolutions of graph signals converge to lipschitz graphon filters
network diffusion process
readout layer
spectral graph filter
spectral representation of a convolutional graph filter
spectral representation of graphon convolutions
We can verify whether graphs without node features and different laplacian eigenvalues are not isomorphic