Exploring network structures in feature space

TL;DR

This paper introduces a multi-phase method that analyzes network structures by transforming network data into a feature space, enabling easier detection of various structural patterns through clustering.

Contribution

The novel approach maps network similarity measures into a lower-dimensional feature space for flexible and effective network structure analysis.

Findings

Enables detection of diverse network structures.

Circumvents micro-structure and scale difficulties.

Applicable to general and community structures.

Abstract

We propose a multi-phase approach to explore network structures. In this method, structure analysis is not carried out on the observed network directly. Instead, certain similarity measures of the nodes are derived from the network firstly, which are then projected onto an appropriate lower-dimensional feature space. The clustering structure can be defined in the feature space, and analyzed by conventional clustering algorithms. The classified data are finally mapped back to the original network space if necessary to complete the analysis of network structures. By mapping onto the feature space, some difficulties due to the diversity of micro-structures and scale of the network can be circumvented. This makes it possible for the proposed method to deal with more general structures such as detecting groups in a random background, as well as identifying usual community structures in networks.