Inversion method for content-based networks

TL;DR

This paper extends an Expectation Maximization method to analyze content-based networks, enabling the recovery of underlying community or multipartite structures even with randomness, and introduces entropy measures to evaluate classification quality.

Contribution

The paper generalizes the EM method for graphs to content-based networks and introduces entropy metrics to assess classification accuracy and structure recovery.

Findings

The generalized EM method effectively recovers network generation processes.

Entropy measures Sq and Sc evaluate classification quality and determine optimal class number.

The method performs well even with randomness in network connections.

Abstract

In this paper, we generalize a recently introduced Expectation Maximization (EM) method for graphs and apply it to content-based networks. The EM method provides a classification of the nodes of a graph, and allows to infer relations between the different classes. Content-based networks are ideal models for graphs displaying any kind of community or/and multipartite structure. We show both numerically and analytically that the generalized EM method is able to recover the process that led to the generation of such networks. We also investigate the conditions under which our generalized EM method can recover the underlying contents-based structure in the presence of randomness in the connections. Two entropies, Sq and Sc, are defined to measure the quality of the node classification and to what extent the connectivity of a given network is content-based. Sq and Sc are also useful in determining the number of classes for which the classification is optimal.