Predicting Anchor Links between Heterogeneous Social Networks

TL;DR

This paper introduces a novel meta-path-based approach called CRMP for predicting anchor links between different social networks, addressing the challenge of missing target users in the prediction process.

Contribution

The paper proposes the CRMP method utilizing meta-paths to effectively predict anchor links between heterogeneous social networks, a problem not addressed by traditional link prediction techniques.

Findings

CRMP outperforms recent methods in real-world experiments.

Meta-paths effectively capture social factors influencing anchor link formation.

The approach demonstrates high accuracy in heterogeneous network scenarios.

Abstract

People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. Many new social networks try to attract users of other existing networks to increase the number of their users. Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks. In this paper, we concentrated on predicting the formation of such anchor links between heterogeneous social networks. Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created. To solve this problem, we use meta-paths as a powerful tool for utilizing heterogeneous information in both the source and target networks. To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP). By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link. Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.