Enhancing Stratified Graph Sampling Algorithms based on Approximate Degree Distribution

TL;DR

This paper introduces a novel stratified graph sampling approach based on approximate degree distribution, improving the accuracy and efficiency of sampling in scale-free networks.

Contribution

It proposes a new stratified sampling strategy using approximate degree distribution and develops two algorithms that outperform existing methods in preserving graph properties.

Findings

Sampling algorithms better preserve graph properties.

Algorithms are more unbiased and efficient.

Outperform state-of-the-art methods in experiments.

Abstract

Sampling technique has become one of the recent research focuses in the graph-related fields. Most of the existing graph sampling algorithms tend to sample the high degree or low degree nodes in the complex networks because of the characteristic of scale-free. Scale-free means that degrees of different nodes are subject to a power law distribution. So, there is a significant difference in the degrees between the overall sampling nodes. In this paper, we propose an idea of approximate degree distribution and devise a stratified strategy using it in the complex networks. We also develop two graph sampling algorithms combining the node selection method with the stratified strategy. The experimental results show that our sampling algorithms preserve several properties of different graphs and behave more accurately than other algorithms. Further, we prove the proposed algorithms are superior to the off-the-shelf algorithms in terms of the unbiasedness of the degrees and more efficient than state-of-the-art FFS and ES-i algorithms.