Quantifying and qualifying trust: Spectral decomposition of trust networks

TL;DR

This paper introduces a spectral decomposition approach to analyze trust networks, aiming to identify trust sources and enhance security without disrupting the trust distribution, linking trust modeling with graph clustering techniques.

Contribution

It presents a novel spectral method for mining trust sources in networks, connecting trust analysis with spectral graph theory and data mining.

Findings

Spectral decomposition reveals trust sources in networks.

Trust distribution remains stable while identifying key trust contributors.

Links trust network analysis with graph clustering and classification theory.

Abstract

In a previous FAST paper, I presented a quantitative model of the process of trust building, and showed that trust is accumulated like wealth: the rich get richer. This explained the pervasive phenomenon of adverse selection of trust certificates, as well as the fragility of trust networks in general. But a simple explanation does not always suggest a simple solution. It turns out that it is impossible to alter the fragile distribution of trust without sacrificing some of its fundamental functions. A solution for the vulnerability of trust must thus be sought elsewhere, without tampering with its distribution. This observation was the starting point of the present paper. It explores a different method for securing trust: not by redistributing it, but by mining for its sources. The method used to break privacy is thus also used to secure trust. A high level view of the mining methods that connect the two is provided in terms of *similarity networks*, and *spectral decomposition* of similarity preserving maps. This view may be of independent interest, as it uncovers a common conceptual and structural foundation of mathematical classification theory on one hand, and of the spectral methods of graph clustering and data mining on the other hand.