Estimation of Graph Features Based on Random Walks Using Neighbors' Properties

TL;DR

This paper presents a new algorithm for estimating graph features from social networks using random walks, optimizing for fewer API calls while maintaining high accuracy, validated through experiments on known graphs.

Contribution

Introduces a cost-aware feature estimation algorithm that reduces API calls and improves accuracy over existing methods in social network sampling.

Findings

The proposed algorithm outperforms existing methods in accuracy.

Fewer API calls are required without sacrificing estimation quality.

Experimental results validate the effectiveness of the new approach.

Abstract

Using random walks for sampling has proven advantageous in assessing the characteristics of large and unknown social networks. Several algorithms based on random walks have been introduced in recent years. In the practical application of social network sampling, there is a recurrent reliance on an application programming interface (API) for obtaining adjacent nodes. However, owing to constraints related to query frequency and associated API expenses, it is preferable to minimize API calls during the feature estimation process. In this study, considering the acquisition of neighboring nodes as a cost factor, we introduce a feature estimation algorithm that outperforms existing algorithms in terms of accuracy. Through experiments that simulate sampling on known graphs, we demonstrate the superior accuracy of our proposed algorithm when compared to existing alternatives.