AKBR: Learning Adaptive Kernel-based Representations for Graph
  Classification
Feifei Qian Lixin Cui Yue Wang Hangyuan Du Lu Bai Edwin R. Hancock
In this paper, we propose a new model to learn Adaptive Kernel-based
Representations (AKBR) for graph classification. Unlike state-of-the-art
R-convolution graph kernels that are defined by merely counting any pair of
isomorphic substructures between graphs and cannot provide an end-to-end
learning mechanism for the classifier, the proposed AKBR approach aims to
define an end-to-end representation learning model to construct an adaptive
kernel matrix for graphs. To this end, we commence by leveraging a novel
feature-channel attention mechanism to capture the interdependencies between
different substructure invariants of original graphs. The proposed AKBR model
can thus effectively identify the structural importance of different
substructures, and compute the R-convolution kernel between pairwise graphs
associated with the more significant substructures specified by their
structural attentions. Since each row of the resulting kernel matrix can be
theoretically seen as the embedding vector of a sample graph, the proposed AKBR
model is able to directly employ the resulting kernel matrix as the graph
feature matrix and input it into the classifier for classification (i.e., the
SoftMax layer), naturally providing an end-to-end learning architecture between
the kernel computation as well as the classifier. Experimental results show
that the proposed AKBR model outperforms existing state-of-the-art graph
kernels and deep learning methods on standard graph benchmarks.