# how can we represent a Big Data set in Graph Signals. (Big Data in Graph signal Processing)

Hi

I have a couple of questions :

first of all, consider a big dataset which is given to us, it has 150 rows and 365 columns which are sensor measurements and days of one year respectively($D$).

In Example Application section it tries to represent the big dataset in the product of two graphs (the article introduces 3 types of products but I think the dataset have been tested on the Cartesian product). It breake the data set into two graphs (time series($T$) with 365 nodes and the sensor network($A$) with 150 nodes).

Because there was no tag named Graph signal processing, I could not mention it.

the article has represented this two graph in FIG1 a,b.

Now my question :

if I correctly understand!

if there was no time we just need to decompose the sensor matrix ($A$) and after that, we could take Fourier transform, now during the time we just have more samples from our sensors so why we should eig-decompose our product($T \cdot A$) instead of sensor network($A$). I mean how increasing the number of sensor data from our sensors, represents itself in graph product.

or why matrix $D$ can be broken to ${T, A}$?(and definitely ${T,A}$ can be processed parallelly)