# What exactly is a grid-like topology according to the book Deep Learning?

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?

In graphs, nodes and edges do not usually have a specified position/orientation: for example, we cannot say that node $$A$$ is to the right/left of node $$B$$ because edges do not typically have an orientation. In grid-like structures, like images, we could say that about pixels. In a graph, all we know is that nodes are connected to other nodes via some edges, which may have weights, but, again, no orientation.