7 research outputs found
Maximal Independent Sets for Pooling in Graph Neural Networks
Convolutional Neural Networks (CNNs) have enabled major advances in image
classification through convolution and pooling. In particular, image pooling
transforms a connected discrete lattice into a reduced lattice with the same
connectivity and allows reduction functions to consider all pixels in an image.
However, there is no pooling that satisfies these properties for graphs. In
fact, traditional graph pooling methods suffer from at least one of the
following drawbacks: Graph disconnection or overconnection, low decimation
ratio, and deletion of large parts of graphs. In this paper, we present three
pooling methods based on the notion of maximal independent sets that avoid
these pitfalls. Our experimental results confirm the relevance of maximal
independent set constraints for graph pooling
Maximal Independent Vertex Set applied to Graph Pooling
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
Maximal Independent Vertex Set applied to Graph Pooling
International audienceConvolutional 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
Maximal Independent Sets for Pooling in Graph Neural Networks
International audienceConvolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete lattice into a reduced lattice with the same connectivity and allows reduction functions to consider all pixels in an image. However, there is no pooling that satisfies these properties for graphs. In fact, traditional graph pooling methods suffer from at least one of the following drawbacks: Graph disconnection or overconnection, low decimation ratio, and deletion of large parts of graphs. In this paper, we present three pooling methods based on the notion of maximal independent sets that avoid these pitfalls. Our experimental results confirm the relevance of maximal independent set constraints for graph pooling