Social Graph Restoration via Random Walk Sampling

TL;DR

This paper introduces a novel method for reconstructing social graphs from limited random walk samples, accurately preserving local and global structural properties for better analysis.

Contribution

The paper presents a new graph restoration technique that outperforms existing methods in accurately reproducing original social graph structures from limited data.

Findings

More accurate reproduction of local structural properties

Better preservation of global graph structure

Improved visual similarity to original graphs

Abstract

Analyzing social graphs with limited data access is challenging for third-party researchers. To address this challenge, a number of algorithms that estimate structural properties via a random walk have been developed. However, most existing algorithms are limited to the estimation of local structural properties. Here we propose a method for restoring the original social graph from the small sample obtained by a random walk. The proposed method generates a graph that preserves the estimates of local structural properties and the structure of the subgraph sampled by a random walk. We compare the proposed method with subgraph sampling using a crawling method and the existing method for generating a graph that structurally resembles the original graph via a random walk. Our experimental results show that the proposed method more accurately reproduces the local and global structural properties on average and the visual representation of the original graph than the compared methods. We expect that our method will lead to exhaustive analyses of social graphs with limited data access.