A fast hybrid entropy-attribute diversity sampling based graph kernel

Abstract

Graph kernels have become a cornerstone in the analysis of graph-structured data, offering powerful tools for similarity assessment in various domains. However, existing graph kernel methods often grapple with efficiently capturing both the structural complexity and attribute diversity inherent in graphs. This paper introduces the “Hybrid Entropy-Attribute Diversity Sampling Graph Kernel” (HEADS), a novel approach that synergizes entropy-based analysis with attribute-diversity-driven sampling to address these challenges. Our method leverages the Von Neumann entropy to quantify the informational content and complexity of graph structures, enhancing the expressiveness of the kernel. Additionally, we introduce an innovative attribute-diversity-driven snowball sampling technique, which ensures a comprehensive and representative selection of graph features. The integration of entropy measures with attribute diversity in our kernel computation marks a significant advancement in graph kernel analysis, paving the way for its application in large-scale, real-world graph data scenarios. This paper details the formulation of the HEADS approach, its algorithmic implementation, and an extensive evaluation demonstrating its efficacy in both computational performance and classification accuracy.