Estimating Properties of Social Networks via Random Walk considering Private Nodes

TL;DR

This paper introduces novel random walk-based algorithms for estimating social network properties that effectively handle private nodes, significantly improving accuracy over existing methods.

Contribution

The paper proposes new algorithms that incorporate neighbor selection and weighting strategies to accurately estimate network properties despite private nodes.

Findings

Algorithms improve estimation accuracy by up to 92.6% on real datasets.

Proposed methods effectively mitigate bias caused by private nodes.

New weighting techniques reduce sampling bias and estimation errors.

Abstract

Accurately analyzing graph properties of social networks is a challenging task because of access limitations to the graph data. To address this challenge, several algorithms to obtain unbiased estimates of properties from few samples via a random walk have been studied. However, existing algorithms do not consider private nodes who hide their neighbors in real social networks, leading to some practical problems. Here we design random walk-based algorithms to accurately estimate properties without any problems caused by private nodes. First, we design a random walk-based sampling algorithm that comprises the neighbor selection to obtain samples having the Markov property and the calculation of weights for each sample to correct the sampling bias. Further, for two graph property estimators, we propose the weighting methods to reduce not only the sampling bias but also estimation errors due to private nodes. The proposed algorithms improve the estimation accuracy of the existing algorithms by up to 92.6% on real-world datasets.