Collective geographical embedding for geolocating social network users

Fengjiao Wang, Chun Ta Lu, Yongzhi Qu, Philip S. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


Inferring the physical locations of social network users is one of the core tasks in many online services, such as targeted advertisement, recommending local events, and urban computing. In this paper, we introduce the Collective Geographical Embedding (CGE) algorithm to embed multiple information sources into a low dimensional space, such that the distance in the embedding space reflects the physical distance in the real world. To achieve this, we introduced an embedding method with a location affinity matrix as a constraint for heterogeneous user network. The experiments demonstrate that the proposed algorithm not only outperforms traditional user geolocation prediction algorithms by collectively extracting relations hidden in the heterogeneous user network, but also outperforms state-of-the-art embedding algorithms by appropriately casting geographical information of check-in.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319574530
StatePublished - 2017
Externally publishedYes
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: May 23 2017May 26 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
This work is supported in part by NSF through grants IIS-1526499, and CNS-1626432, and NSFC 61672313. Yongzhi Qu would like to acknowledge national natural science foundation of China (NSFC 51505353).

Publisher Copyright:
© 2017, Springer International Publishing AG.


  • Geolocation
  • Geometric regularization
  • Geometrical embedding


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