A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on gra...

Full description

Bibliographic Details
Main Authors: Xinyu Huang, Dongming Chen, Tao Ren
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Symmetry
Subjects:
social network
temporal links prediction
gravity model
multilayer network
Online Access:https://www.mdpi.com/2073-8994/12/1/100
id doaj-893a065c568740f88e8e1b72db738dbf
record_format Article
spelling doaj-893a065c568740f88e8e1b72db738dbf2020-11-25T02:20:44ZengMDPI AGSymmetry2073-89942020-01-0112110010.3390/sym12010100sym12010100A Feasible Temporal Links Prediction Framework Combining with Improved Gravity ModelXinyu Huang0Dongming Chen1Tao Ren2Software College, Northeastern University, Shenyang 110169, ChinaSoftware College, Northeastern University, Shenyang 110169, ChinaSoftware College, Northeastern University, Shenyang 110169, ChinaSocial network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.https://www.mdpi.com/2073-8994/12/1/100social networktemporal links predictiongravity modelmultilayer network
collection DOAJ
language English
format Article
sources DOAJ
author Xinyu Huang
Dongming Chen
Tao Ren
spellingShingle Xinyu Huang
Dongming Chen
Tao Ren
A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
Symmetry
social network
temporal links prediction
gravity model
multilayer network
author_facet Xinyu Huang
Dongming Chen
Tao Ren
author_sort Xinyu Huang
title A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
title_short A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
title_full A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
title_fullStr A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
title_full_unstemmed A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
title_sort feasible temporal links prediction framework combining with improved gravity model
publisher MDPI AG
series Symmetry
issn 2073-8994
publishDate 2020-01-01
description Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.
topic social network
temporal links prediction
gravity model
multilayer network
url https://www.mdpi.com/2073-8994/12/1/100
work_keys_str_mv AT xinyuhuang afeasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
AT dongmingchen afeasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
AT taoren afeasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
AT xinyuhuang feasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
AT dongmingchen feasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
AT taoren feasibletemporallinkspredictionframeworkcombiningwithimprovedgravitymodel
_version_ 1724870260073431040

Similar Items