Motif Entropy Graph Kernel

doi:10.1016/j.patcog.2023.109544

Motif Entropy Graph Kernel

2023-08-01

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DOI：10.1016/j.patcog.2023.109544

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Abstract

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Graph kernels have achieved excellent performance in graph classification tasks. In this paper, we propose a novel deep motif entropy graph kernel for the purpose of graph classification. For better capturing the differences between substructures, we gauge detailed information through a family of K-layer expansion motifs rooted at each node and combine the Weisfeiler-Lehman algorithm to subdivide motifs, which is further enhanced by motif entropy. Experiments on eight graph-structured datasets demonstrate that our method is able to outperform the state-of-the-art kernel methods for the tasks of graph classification.(c) 2023 Elsevier Ltd. All rights reserved.

Keywords:

Graph representation

Motif entropy

Graph kernel

Wasserstein distance

Motif Entropy Graph Kernel

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