No... a graph convolution is just a convolution (over a graph, like all convolutions).

The difference from a "normal" convolution is that you can consider arbitrary connectivity of the graph (rather than the usual connectivity induced by a regular Euclidian grid), but the underlying idea is the same: to calculate the result of the operation at any single place (i.e., node), you need to perform a linear operation over that place (i.e., node) and its neighbourhood (i.e., connected nodes), the same way that (e.g.) in a convolutional neural network, you calculate the value of a pixel by considering its value and that of its neighbours, when performing a convolution.