AKBR: Learning Adaptive Kernel-based Representations for Graph Classification

arXiv:2403.16130v1 Announce Type: new
Abstract: 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 …

abstract art arxiv classification classifier convolution cs.ai cs.lg graph graphs kernel learn paper state type