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Assignment kernels are based on an optimal bijection between the parts and have proven to be an effective alternative to the established convolution kernels.
Several different types of graph neural network models have been introduced for learning the representations from such different types of graphs already.
There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.
The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.
To solve this problem, one usually calculates a low-dimensional representation for each node in the graph with supervised or unsupervised approaches.
Graph Neural Networks (GNNs) are powerful to learn the representation of graph-structured data.
In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs.
The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.