Mining Essential Relationships under Multiplex Networks

TL;DR

This paper introduces a new metric called 'similarity rate' to analyze and uncover essential relationships among nodes in multiplex networks, demonstrated through experiments on a terrorist dataset.

Contribution

The paper proposes a novel 'similarity rate' metric for identifying hidden essential relationships in multiplex networks, advancing analysis of heterogeneous data.

Findings

Similarity rate effectively uncovers essential relationships.

The metric performs well on Indonesian Terrorists dataset.

Provides a new tool for multiplex network analysis.

Abstract

In big data times, massive datasets often carry different relationships among the same group of nodes, analyzing on these heterogeneous relationships may give us a window to peek the essential relationships among nodes. In this paper, first of all we propose a new metric "similarity rate" in order to capture the changing rate of similarities between node-pairs though all networks; secondly, we try to use this new metric to uncover essential relationships between node-pairs which essential relationships are often hidden and hard to get. From experiments study of Indonesian Terrorists dataset, this new metric similarity rate function well for giving us a way to uncover essential relationships from lots of appearances.