Factor Graph Model Based User Profile Matching Across Social Networks

Lidong Wang,Shihua Cao,Keyong Hu,Yun Zhang

doi:10.1109/access.2019.2948073

Lidong Wang, Shihua Cao + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2948073

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Abstract

In modern society, it is common for people to be active in many different online social networks at once. As new social network services arise every year, it remains a great challenge to integrate social data. Discovering multiple profiles of a single person across different social networks is a precondition for integration, but it is still challenging due to the inconsistency and disruption of the accessible information among social media networks (SMNs). Many studies have made efforts on user's profiles, users' contents, and network structure to address this issue, but the issue of how to consider all these information in a unified model and tackle them simultaneously still remains challenging. Considering that identical users tend to have partial similar friend relationship structures in different SMNs, especially friendship SMNs, we deepen the analysis of “friend” relationships (mutual following connections) in different SMNs, and propose PIFGM (Pairwise Identical Factor Graph Model), a novel factor graph model-based model, to address this problem by considering both user attributes and friend relationships across networks. We also present a distributed learning algorithm to handle large-scale social networks. We evaluate the proposed model on two different data collections: SNS and SR. Our experimental results validate the effectiveness and efficiency of the proposed model. The proposed PIFGM significantly outperforms several alternative methods by up to approximately 10%~20% in terms of F1 and precision on SNS and SR respectively.

Highlights

In recent years, users have been introduced to many online social networks such as Sina microblog, Twitter, Instagram, or LinkedIn
To quantitatively evaluate the proposed model, we consider the following performance metrics: if a method can find a matching between the two networks, we say that the method correctly recognizes a matching; otherwise, we say that the method makes a wrong recognition
The above performance demonstrates that our method considers both dyadic friend relationship and triadic friend relationship can identify more potential identical users

Summary

INTRODUCTION

Users have been introduced to many online social networks such as Sina microblog, Twitter, Instagram, or LinkedIn. Zhang et al [10] proposed an energy based model to formalize the problem as a unified framework, it mainly focuses on the global consistency on user identification tasks among multiple SMNs. In this paper, we hope to formulate local accessible attributes and friend relationship matching into a novel principled optimization model. Soroush et al [22] was inspired by stylometry techniques and presented the models for Digital Stylometry to match user accounts Such content-based features are often sparse in SMNs. public profile attributes provide powerful information for user identification, some attributes are difficult to obtain because of privacy protection. (2) JLA utilizes Conditional Random Field that combines usage of profile attributes and social linkage, COSNET utilizes energy-based model by considering both local and global consistency among multiple networks, while PIFGM utilizes factor graph model to formalize the task as a as a unified optimization framework. We propose a distributed learning method based on MPI without information loss

PAIRWISE IDENTICAL FACTOR GRAPH MODEL

FRIEND RELATIONSHIP MATCHING

MODEL LEARNING

EXPERIMENTAL RESULTS

DATASET We perform experiments on two collections

CONCLUSION