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

Authors

Melissa Marchand, Kyle Gallivan, Wen Huang, Paul Van Dooren*

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.

Key words

Role extraction; blockmodeling; similarity measure; neighborhood patterns; community detection; overlapping communities; bipartite networks; signed networks; weighted networks;

Status

Submitted.

BibTex entry

Technical Report
@TECHREPORT{MHGV2020,
author = "Melissa Marchand and Kyle Gallivan and Wen Huang and Paul Van Dooren*",
title = "Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem",
year = 2020,
}