The blind leading the blind: Network-based location estimation under uncertainty

Eric Malmi*, Arno Solin, Aristides Gionis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

We propose a probabilistic method for inferring the geographical locations of linked objects, such as users in a social network. Unlike existing methods, our model does not assume that the exact locations of any subset of the linked objects, like neighbors in a social network, are known. The method efficiently leverages prior knowledge on the locations, resulting in high geolocation accuracies even if none of the locations are initially known. Experiments are conducted for three scenarios: geolocating users of a location-based social network, geotagging historical church records, and geotagging Flickr photos. In each experiment, the proposed method outperforms two state-of-the-art network-based methods. Furthermore, the last experiment shows that the method can be employed not only to network-based but also to content-based location estimation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAnnalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares
Pages406-421
Number of pages16
Volume9285
ISBN (Electronic)978-3-319-23525-7
DOIs
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Porto, Portugal
Duration: 7 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9285
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD
CountryPortugal
CityPorto
Period07/09/201511/09/2015

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