Large-Scale Parallel Matching of Social Network Profiles

TL;DR

This paper introduces a scalable, minimally supervised profile matching algorithm that accurately links user identities across social networks like Facebook and VKontakte, with high precision and efficiency.

Contribution

It presents a novel parallelizable matching method achieving high precision and low supervision, suitable for large-scale social network profile matching.

Findings

Precision of 0.98 achieved

Recall of 0.54 achieved

Method is computationally efficient and parallelizable

Abstract

A profile matching algorithm takes as input a user profile of one social network and returns, if existing, the profile of the same person in another social network. Such methods have immediate applications in Internet marketing, search, security, and a number of other domains, which is why this topic saw a recent surge in popularity. In this paper, we present a user identity resolution approach that uses minimal supervision and achieves a precision of 0.98 at a recall of 0.54. Furthermore, the method is computationally efficient and easily parallelizable. We show that the method can be used to match Facebook, the most popular social network globally, with VKontakte, the most popular social network among Russian-speaking users.