Efficient Sampling for Better OSN Data Provisioning

TL;DR

This paper introduces a method for OSNs to provide tunable, weighted samples of user graphs in non-overlapping increments, improving data provision efficiency and accuracy for graph analysis.

Contribution

It presents a novel sampling technique that allows OSNs to release scalable, weighted graph samples with controlled size and minimal overlap, enhancing data utility.

Findings

Enables OSNs to provide scalable graph samples

Improves accuracy of graph feature estimation

Reduces resource consumption in data sampling

Abstract

Data concerning the users and usage of Online Social Networks (OSNs) has become available externally, from public resources (e.g., user profiles), participation in OSNs (e.g., establishing relationships and recording transactions such as user updates) and APIs of the OSN provider (such as the Twitter API). APIs let OSN providers monetize the release of data while helping control measurement load, e.g. by providing samples with different cost-granularity tradeoffs. To date, this approach has been more suited to releasing transactional data, with graphical data still being obtained by resource intensive methods such a graph crawling. In this paper, we propose a method for OSNs to provide samples of the user graph of tunable size, in non-intersecting increments, with sample selection that can be weighted to enhance accuracy when estimating different features of the graph.