Convolutional neural networks (CNN) have enabled major advances in image
classification through convolution and pooling. In particular, image pooling
transforms a connected discrete grid into a reduced grid with the same
connectivity and allows reduction functions to take into account all the pixels
of an image. However, a pooling satisfying such properties does not exist for
graphs. Indeed, some methods are based on a vertex selection step which induces
an important loss of information. Other methods learn a fuzzy clustering of
vertex sets which induces almost complete reduced graphs. We propose to
overcome both problems using a new pooling method, named MIVSPool. This method
is based on a selection of vertices called surviving vertices using a Maximal
Independent Vertex Set (MIVS) and an assignment of the remaining vertices to
the survivors. Consequently, our method does not discard any vertex information
nor artificially increase the density of the graph. Experimental results show
an increase in accuracy for graph classification on various standard datasets