Friend closeness based user matching cross social networks

Tinghuai Ma; Lei Guo; Xin Wang; Yurong Qian; Yuan Tian; Najla Al-Nabhan; Tinghuai Ma; Lei Guo; Xin Wang; Yurong Qian; Yuan Tian; Najla Al-Nabhan

doi:10.3934/mbe.2021214

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4264-4292. doi: 10.3934/mbe.2021214

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Friend closeness based user matching cross social networks

1.
Nanjing University of information science Technology, Nanjing 210044, China
2.
Huafeng Meteorological Media Group, Beijing 100080, China
3.
Xinjiang University, Urumqi 830008, China
4.
Nanjing Institute of Technology, Jiangsu, Nanjing 211167, China
5.
Department Computer Science, KingSaud University, Riyadh 11362, Saudi Arabia

Received: 22 January 2021 Accepted: 27 April 2021 Published: 17 May 2021

The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.
- user matching,
- cross networks,
- friend closeness,
- network embedding,
- attention mechanism
Citation: Tinghuai Ma, Lei Guo, Xin Wang, Yurong Qian, Yuan Tian, Najla Al-Nabhan. Friend closeness based user matching cross social networks[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4264-4292. doi: 10.3934/mbe.2021214

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Abstract

The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.

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