Hierarchical stochastic graphlet embedding for graph-based pattern recognition

TL;DR

This paper introduces a hierarchical stochastic graphlet embedding method that captures structural information more effectively for graph-based pattern recognition, outperforming existing techniques on benchmark datasets.

Contribution

It proposes a novel hierarchical graph embedding approach combining graph clustering and stochastic graphlet embedding to preserve structural information.

Findings

Outperforms state-of-the-art graph embedding methods on benchmark datasets.

Effectively captures hierarchical and structural information in graphs.

Provides a more robust graph representation for pattern recognition.

Abstract

Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes, and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the Stochastic Graphlet Embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low to high order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.