I am reading this book called "Deep Learning" (by Goodfellow, Bengio and Courville).
On page 326, in the first paragraph, it says:
CNNs, are a specialized kind of neural network for processing data that has a known grid-like topology. Examples include time-series data, which can be thought of as a 1-D grid taking samples at regular time intervals, and image data, which can be thought of as a 2-D grid of pixel
Considering an image as a grid is completely intuitive. And, similarly, we can extend the logic to a 1-D time series.
But then what cannot be considered as having a grid-like structure?