Friend closeness based user matching cross social networks
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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2021-05-01
|
Series: | Mathematical Biosciences and Engineering |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2021214?viewType=HTML |
id |
doaj-baec8f8eb7a2425e9476e524cdfccc0c |
---|---|
record_format |
Article |
spelling |
doaj-baec8f8eb7a2425e9476e524cdfccc0c2021-06-10T02:01:03ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-05-011844264429210.3934/mbe.2021214Friend closeness based user matching cross social networksTinghuai Ma0Lei Guo 1Xin Wang 2Yurong Qian3Yuan Tian 4Najla Al-Nabhan 51. Nanjing University of information science Technology, Nanjing 210044, China1. Nanjing University of information science Technology, Nanjing 210044, China2. Huafeng Meteorological Media Group, Beijing 100080, China3. Xinjiang University, Urumqi 830008, China4. Nanjing Institute of Technology, Jiangsu, Nanjing 211167, China5. Department Computer Science, KingSaud University, Riyadh 11362, Saudi ArabiaThe 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.https://www.aimspress.com/article/doi/10.3934/mbe.2021214?viewType=HTMLuser matchingcross networksfriend closenessnetwork embeddingattention mechanism |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tinghuai Ma Lei Guo Xin Wang Yurong Qian Yuan Tian Najla Al-Nabhan |
spellingShingle |
Tinghuai Ma Lei Guo Xin Wang Yurong Qian Yuan Tian Najla Al-Nabhan Friend closeness based user matching cross social networks Mathematical Biosciences and Engineering user matching cross networks friend closeness network embedding attention mechanism |
author_facet |
Tinghuai Ma Lei Guo Xin Wang Yurong Qian Yuan Tian Najla Al-Nabhan |
author_sort |
Tinghuai Ma |
title |
Friend closeness based user matching cross social networks |
title_short |
Friend closeness based user matching cross social networks |
title_full |
Friend closeness based user matching cross social networks |
title_fullStr |
Friend closeness based user matching cross social networks |
title_full_unstemmed |
Friend closeness based user matching cross social networks |
title_sort |
friend closeness based user matching cross social networks |
publisher |
AIMS Press |
series |
Mathematical Biosciences and Engineering |
issn |
1551-0018 |
publishDate |
2021-05-01 |
description |
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. |
topic |
user matching cross networks friend closeness network embedding attention mechanism |
url |
https://www.aimspress.com/article/doi/10.3934/mbe.2021214?viewType=HTML |
work_keys_str_mv |
AT tinghuaima friendclosenessbasedusermatchingcrosssocialnetworks AT leiguo friendclosenessbasedusermatchingcrosssocialnetworks AT xinwang friendclosenessbasedusermatchingcrosssocialnetworks AT yurongqian friendclosenessbasedusermatchingcrosssocialnetworks AT yuantian friendclosenessbasedusermatchingcrosssocialnetworks AT najlaalnabhan friendclosenessbasedusermatchingcrosssocialnetworks |
_version_ |
1721386437394300928 |