Abstract

A current challenge in graph clustering is to tackle the issue of complex networks, i.e, graphs with attributed vertices and/or edges. In this paper, we present GraphTrees, a novel method that relies on random decision trees to compute pairwise dissimilarities between vertices in a graph. We show that using different types of trees, it is possible to extend this framework to graphs where the vertices have attributes. While many existing methods that tackle the problem of clustering vertices in an attributed graph are limited to categorical attributes, GraphTrees can handle heterogeneous types of vertex attributes. Moreover, unlike other approaches, the attributes do not need to be preprocessed. We also show that our approach is competitive with well-known methods in the case of non-attributed graphs in terms of quality of clustering, and provides promising results in the case of vertex-attributed graphs. By extending the use of an already well established approach – the random trees – to graphs, our proposed approach opens new research directions, by leveraging decades of research on this topic.

Highlights

  • Main Contributions of the Paper: 1. We propose a first step to bridge the gap between random decision trees and graph clustering and extend it to vertex attributed graphs (Subsect. 4.1)

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  • We presented a method based on the construction of random trees to compute dissimilarities between graph vertices, called GT

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Summary

Introduction

Identifying community structure in graphs is a challenging task in many applications: computer networks, social networks, etc. Graphs have an expressive power that enables an efficient representation of relations between objects as well as their properties. Attributed graphs where vertices or edges are endowed with a set of attributes are widely available, many of them being created and curated by the semantic web community. While these so-called knowledge graphs contain a lot of information, their exploration can be challenging in practice. Common approaches to find communities in such graphs rely on rather complex transformations of the input graph. Common approaches to find communities in such graphs rely on rather complex transformations of the input graph. 1 many definitions can be found in the literature [9].

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