Inferring individual physical locations with social friendships

Meng Zhou; Wei Tu; Qingquan Li; Yang Yue; Xiaomeng Chang

doi:10.1109/GEOINFORMATICS.2015.7378565

Inferring individual physical locations with social friendships

Meng Zhou
, Wei Tu^*
, Qingquan Li
, Yang Yue
, Xiaomeng Chang

^*Corresponding author for this work

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Physical location is an important characteristic for digital individuals, as it is widely used in location based services, such as navigation, advertisements, and recommendations. This paper focuses on the problem of inferring individual physical locations from their friendships in a social network. We represent individual locations with a few high frequency places to eliminate the noise influence. By using of interactions between users, a spatial based inferring model is developed to directly estimate individual physical locations. The spatial weighted clustering method is used by considering the structure of interactions between friends. Data from Tencent, the biggest social network service provider in China, is used to conduct an experiment to validate the performance of the proposed inferring framework. Results indicate the framework can predict individual locations within 15 km in distance error with the accuracy of 68%.

Original language	English
Title of host publication	Proceedings - 23rd International Conference on Geoinformatics 2015, Geoinformatics 2015
Editors	Shixiong Hu, Xinyue Ye
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781467376631, 9781467376624
DOIs	https://doi.org/10.1109/GEOINFORMATICS.2015.7378565
Publication status	Published - 19 Jun 2015
Event	23rd International Conference on Geoinformatics, Geoinformatics 2015 - Wuhan, China Duration: 19 Jun 2015 → 21 Jun 2015

Publication series

Name	International Conference on Geoinformatics
ISSN (Print)	2161-024X
ISSN (Electronic)	2161-0258

Conference

Conference	23rd International Conference on Geoinformatics, Geoinformatics 2015
Country/Territory	China
City	Wuhan
Period	19/06/15 → 21/06/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

data mining
friendship
Physical location
social network
spatial cluster

Access to Document

10.1109/GEOINFORMATICS.2015.7378565

Cite this

@inproceedings{016303482afb44108357b782943612d4,

title = "Inferring individual physical locations with social friendships",

abstract = "Physical location is an important characteristic for digital individuals, as it is widely used in location based services, such as navigation, advertisements, and recommendations. This paper focuses on the problem of inferring individual physical locations from their friendships in a social network. We represent individual locations with a few high frequency places to eliminate the noise influence. By using of interactions between users, a spatial based inferring model is developed to directly estimate individual physical locations. The spatial weighted clustering method is used by considering the structure of interactions between friends. Data from Tencent, the biggest social network service provider in China, is used to conduct an experiment to validate the performance of the proposed inferring framework. Results indicate the framework can predict individual locations within 15 km in distance error with the accuracy of 68\%.",

keywords = "data mining, friendship, Physical location, social network, spatial cluster",

author = "Meng Zhou and Wei Tu and Qingquan Li and Yang Yue and Xiaomeng Chang",

note = "Supported by the National Science Foundation of China (No.4I371377, No.1401444), China Postdoctoral Science Foundation (2014M560671), Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (JCYJ20121019111128765), and the CCF Tencent ARG 20130115, and the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing (13S02). Publisher Copyright: {\textcopyright} 2015 IEEE.; 23rd International Conference on Geoinformatics, Geoinformatics 2015 ; Conference date: 19-06-2015 Through 21-06-2015",

year = "2015",

month = jun,

day = "19",

doi = "10.1109/GEOINFORMATICS.2015.7378565",

language = "English",

series = "International Conference on Geoinformatics",

publisher = "IEEE",

editor = "Shixiong Hu and Xinyue Ye",

booktitle = "Proceedings - 23rd International Conference on Geoinformatics 2015, Geoinformatics 2015",

address = "United States",

}

Zhou, M, Tu, W, Li, Q, Yue, Y & Chang, X 2015, Inferring individual physical locations with social friendships. in S Hu & X Ye (eds), Proceedings - 23rd International Conference on Geoinformatics 2015, Geoinformatics 2015. International Conference on Geoinformatics, IEEE, 23rd International Conference on Geoinformatics, Geoinformatics 2015, Wuhan, China, 19/06/15. https://doi.org/10.1109/GEOINFORMATICS.2015.7378565

Inferring individual physical locations with social friendships. / Zhou, Meng; Tu, Wei; Li, Qingquan et al.
Proceedings - 23rd International Conference on Geoinformatics 2015, Geoinformatics 2015. ed. / Shixiong Hu; Xinyue Ye. IEEE, 2015. (International Conference on Geoinformatics).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Inferring individual physical locations with social friendships

AU - Zhou, Meng

AU - Tu, Wei

AU - Li, Qingquan

AU - Yue, Yang

AU - Chang, Xiaomeng

N1 - Supported by the National Science Foundation of China (No.4I371377, No.1401444), China Postdoctoral Science Foundation (2014M560671), Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (JCYJ20121019111128765), and the CCF Tencent ARG 20130115, and the Open Research Fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing (13S02). Publisher Copyright: © 2015 IEEE.

PY - 2015/6/19

Y1 - 2015/6/19

N2 - Physical location is an important characteristic for digital individuals, as it is widely used in location based services, such as navigation, advertisements, and recommendations. This paper focuses on the problem of inferring individual physical locations from their friendships in a social network. We represent individual locations with a few high frequency places to eliminate the noise influence. By using of interactions between users, a spatial based inferring model is developed to directly estimate individual physical locations. The spatial weighted clustering method is used by considering the structure of interactions between friends. Data from Tencent, the biggest social network service provider in China, is used to conduct an experiment to validate the performance of the proposed inferring framework. Results indicate the framework can predict individual locations within 15 km in distance error with the accuracy of 68%.

AB - Physical location is an important characteristic for digital individuals, as it is widely used in location based services, such as navigation, advertisements, and recommendations. This paper focuses on the problem of inferring individual physical locations from their friendships in a social network. We represent individual locations with a few high frequency places to eliminate the noise influence. By using of interactions between users, a spatial based inferring model is developed to directly estimate individual physical locations. The spatial weighted clustering method is used by considering the structure of interactions between friends. Data from Tencent, the biggest social network service provider in China, is used to conduct an experiment to validate the performance of the proposed inferring framework. Results indicate the framework can predict individual locations within 15 km in distance error with the accuracy of 68%.

KW - data mining

KW - friendship

KW - Physical location

KW - social network

KW - spatial cluster

UR - https://www.scopus.com/pages/publications/84962433247

U2 - 10.1109/GEOINFORMATICS.2015.7378565

DO - 10.1109/GEOINFORMATICS.2015.7378565

M3 - Conference proceeding

AN - SCOPUS:84962433247

T3 - International Conference on Geoinformatics

BT - Proceedings - 23rd International Conference on Geoinformatics 2015, Geoinformatics 2015

A2 - Hu, Shixiong

A2 - Ye, Xinyue

PB - IEEE

T2 - 23rd International Conference on Geoinformatics, Geoinformatics 2015

Y2 - 19 June 2015 through 21 June 2015

ER -

Inferring individual physical locations with social friendships

Abstract

Publication series

Conference

UN SDGs

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this