Multiresolution Mutual Information Method for Social Network Entity Resolution

Cong Shi; Rong Duan

doi:10.1109/ICDMW.2015.94

Multiresolution Mutual Information Method for Social Network Entity Resolution

Cong Shi, Rong Duan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Online Social Networks (OSN) are widely adopted in our daily lives, and it is common for one individual to registerwith multiple sites for different services. Linking the rich contentsof different social network sites is valuable to researchers forunderstanding human behaviors from different perspectives. Forinstance, each OSN has its own group of users and thus, has itsown biases. Linked accounts can be a good calibration dataset toimprove data quality. This Entity Resolution (ER) problem is achallenge in the social network domain that many researchersattempt to tackle. In this paper we take advantage of spatialinformation posted in different social network sites and proposean efficient multiresolution mutual information approach to linkthe entities from those sites. The proposed method significantlyreduces the computing time by utilizing an iterative coarse-tofinemultiresolution approach, yet is robust in dealing with thesparsity of location data. The human location-wise behavior isalso discussed in deciding the resolution level. Public availableTwitter and Instagram data collected from their APIs are usedto illustrate the method, and the performance is evaluated bycomparing it with greedy mutual information approach.

Original language	English (US)
Title of host publication	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Editors	Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	240-247
Number of pages	8
ISBN (Electronic)	9781467384926
DOIs	https://doi.org/10.1109/ICDMW.2015.94
State	Published - Jan 29 2016
Externally published	Yes
Event	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015

Publication series

Name	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/Territory	United States
City	Atlantic City
Period	11/14/15 → 11/17/15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Computer Science Applications

Access to Document

10.1109/ICDMW.2015.94

Cite this

Shi, C., & Duan, R. (2016). Multiresolution Mutual Information Method for Social Network Entity Resolution. In X. Wu, A. Tuzhilin, H. Xiong, J. G. Dy, C. Aggarwal, Z.-H. Zhou, & P. Cui (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 240-247). Article 7395677 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2015.94

Shi, Cong ; Duan, Rong. / Multiresolution Mutual Information Method for Social Network Entity Resolution. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. editor / Xindong Wu ; Alexander Tuzhilin ; Hui Xiong ; Jennifer G. Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 240-247 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

@inproceedings{cf26fea4a738478b8e80c4278e5dad14,

title = "Multiresolution Mutual Information Method for Social Network Entity Resolution",

abstract = "Online Social Networks (OSN) are widely adopted in our daily lives, and it is common for one individual to registerwith multiple sites for different services. Linking the rich contentsof different social network sites is valuable to researchers forunderstanding human behaviors from different perspectives. Forinstance, each OSN has its own group of users and thus, has itsown biases. Linked accounts can be a good calibration dataset toimprove data quality. This Entity Resolution (ER) problem is achallenge in the social network domain that many researchersattempt to tackle. In this paper we take advantage of spatialinformation posted in different social network sites and proposean efficient multiresolution mutual information approach to linkthe entities from those sites. The proposed method significantlyreduces the computing time by utilizing an iterative coarse-tofinemultiresolution approach, yet is robust in dealing with thesparsity of location data. The human location-wise behavior isalso discussed in deciding the resolution level. Public availableTwitter and Instagram data collected from their APIs are usedto illustrate the method, and the performance is evaluated bycomparing it with greedy mutual information approach.",

author = "Cong Shi and Rong Duan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

year = "2016",

month = jan,

day = "29",

doi = "10.1109/ICDMW.2015.94",

language = "English (US)",

series = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "240--247",

editor = "Xindong Wu and Alexander Tuzhilin and Hui Xiong and Dy, {Jennifer G.} and Charu Aggarwal and Zhi-Hua Zhou and Peng Cui",

booktitle = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",

address = "United States",

}

Shi, C & Duan, R 2016, Multiresolution Mutual Information Method for Social Network Entity Resolution. in X Wu, A Tuzhilin, H Xiong, JG Dy, C Aggarwal, Z-H Zhou & P Cui (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015., 7395677, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., pp. 240-247, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDMW.2015.94

Multiresolution Mutual Information Method for Social Network Entity Resolution. / Shi, Cong; Duan, Rong.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 240-247 7395677 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Multiresolution Mutual Information Method for Social Network Entity Resolution

AU - Shi, Cong

AU - Duan, Rong

PY - 2016/1/29

Y1 - 2016/1/29

N2 - Online Social Networks (OSN) are widely adopted in our daily lives, and it is common for one individual to registerwith multiple sites for different services. Linking the rich contentsof different social network sites is valuable to researchers forunderstanding human behaviors from different perspectives. Forinstance, each OSN has its own group of users and thus, has itsown biases. Linked accounts can be a good calibration dataset toimprove data quality. This Entity Resolution (ER) problem is achallenge in the social network domain that many researchersattempt to tackle. In this paper we take advantage of spatialinformation posted in different social network sites and proposean efficient multiresolution mutual information approach to linkthe entities from those sites. The proposed method significantlyreduces the computing time by utilizing an iterative coarse-tofinemultiresolution approach, yet is robust in dealing with thesparsity of location data. The human location-wise behavior isalso discussed in deciding the resolution level. Public availableTwitter and Instagram data collected from their APIs are usedto illustrate the method, and the performance is evaluated bycomparing it with greedy mutual information approach.

AB - Online Social Networks (OSN) are widely adopted in our daily lives, and it is common for one individual to registerwith multiple sites for different services. Linking the rich contentsof different social network sites is valuable to researchers forunderstanding human behaviors from different perspectives. Forinstance, each OSN has its own group of users and thus, has itsown biases. Linked accounts can be a good calibration dataset toimprove data quality. This Entity Resolution (ER) problem is achallenge in the social network domain that many researchersattempt to tackle. In this paper we take advantage of spatialinformation posted in different social network sites and proposean efficient multiresolution mutual information approach to linkthe entities from those sites. The proposed method significantlyreduces the computing time by utilizing an iterative coarse-tofinemultiresolution approach, yet is robust in dealing with thesparsity of location data. The human location-wise behavior isalso discussed in deciding the resolution level. Public availableTwitter and Instagram data collected from their APIs are usedto illustrate the method, and the performance is evaluated bycomparing it with greedy mutual information approach.

UR - http://www.scopus.com/inward/record.url?scp=84964754268&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964754268&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2015.94

DO - 10.1109/ICDMW.2015.94

M3 - Conference contribution

AN - SCOPUS:84964754268

T3 - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

SP - 240

EP - 247

BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

A2 - Wu, Xindong

A2 - Tuzhilin, Alexander

A2 - Xiong, Hui

A2 - Dy, Jennifer G.

A2 - Aggarwal, Charu

A2 - Zhou, Zhi-Hua

A2 - Cui, Peng

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Y2 - 14 November 2015 through 17 November 2015

ER -

Shi C, Duan R. Multiresolution Mutual Information Method for Social Network Entity Resolution. In Wu X, Tuzhilin A, Xiong H, Dy JG, Aggarwal C, Zhou ZH, Cui P, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 240-247. 7395677. (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). doi: 10.1109/ICDMW.2015.94

Multiresolution Mutual Information Method for Social Network Entity Resolution

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this