Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem

TL;DR

This paper analyzes the Neighborhood Pattern Similarity method for role extraction in large graphs, focusing on its theoretical basis and effectiveness in grouping structurally equivalent nodes.

Contribution

It provides a formal analysis of the Neighborhood Pattern Similarity approach, connecting it to ideal graphs and structural equivalence concepts.

Findings

The method effectively identifies nodes with similar roles in large graphs.

The analysis clarifies the conditions under which the similarity measure accurately captures roles.

Insights into the limitations and potential improvements of the approach.

Abstract

In this paper we analyze an indirect approach, called the Neighborhood Pattern Similarity approach, to solve the so-called role extraction problem of a large-scale graph. The method is based on the preliminary construction of a node similarity matrix which allows in a second stage to group together, with an appropriate clustering technique, the nodes that are assigned to have the same role. The analysis builds on the notion of ideal graphs where all nodes with the same role, are also structurally equivalent.