Social Network Integration: Towards Constructing the Social Graph

TL;DR

This paper presents a unified probabilistic framework for integrating multiple social networks into a comprehensive social graph, effectively identifying user correspondences across platforms by leveraging social features and logical constraints.

Contribution

It introduces a novel probabilistic model that combines social features and logical constraints for accurate social network integration, validated through empirical experiments.

Findings

Effective identification of user accounts across networks

Improved accuracy over baseline methods

Validated on real-world social network data

Abstract

In this work, we formulate the problem of social network integration. It takes multiple observed social networks as input and returns an integrated global social graph where each node corresponds to a real person. The key challenge for social network integration is to discover the correspondences or interlinks across different social networks. We engaged an in-depth analysis across three online social networks, AMiner, Linkedin, and Videolectures in order to address what reveals users' social identity, whether the social factors consistent across different social networks and how we can leverage these information to perform integration. We proposed a unified framework for the social network integration task. It crawls data from multiple social networks and further discovers accounts correspond to the same real person from the obtained networks. We use a probabilistic model to determine such correspondence, it incorporates features like the consistency of social status and social ties across different, as well as one-to-one mapping constraint and logical transitivity to jointly make the prediction. Empirical experiments verify the effectiveness of our method.