Inferring Real Mobility in Presence of Fake Check-ins Data

Author:

Gao Qiang1ORCID,Fu Hongzhu1ORCID,Zhang Kunpeng2ORCID,Trajcevski Goce3ORCID,Teng Xu3ORCID,Zhou Fan4ORCID

Affiliation:

1. The Complex Laboratory of New Finance and Economics, Southwestern University of Finance and Economics, China

2. Robert H. Smith School of Business, University of Maryland, USA

3. Iowa State University, USA

4. University of Electronic Science and Technology of China, China

Abstract

Understanding human mobility has become an important aspect of location-based services in tasks such as personalized recommendation and individual moving pattern recognition, enabled by the large volumes of data from geo-tagged social media (GTSM). Prior studies mainly focus on analyzing human historical footprints collected by GTSM and assuming the veracity of the data, which need not hold when some users are not willing to share their real footprints due to privacy concerns—thereby affecting reliability/authenticity. In this study, we address the problem of Inferring Real Mobility (IRMo) of users, from their unreliable historical traces. Tackling IRMo is a non-trivial task due to the: (1) sparsity of check-in data; (2) suspicious counterfeit check-in behaviors; and (3) unobserved dependencies in human trajectories. To address these issues, we develop a novel Graph-enhanced Attention model called IRMoGA , which attempts to capture underlying mobility patterns and check-in correlations by exploiting the unreliable spatio-temporal data. Specifically, we incorporate the attention mechanism (rather than solely relying on traditional recursive models) to understand the regularity of human mobility, while employing a graph neural network to understand the mutual interactions from human historical check-ins and leveraging prior knowledge to alleviate the inferring bias. Our experiments conducted on four real-world datasets demonstrate the superior performance of IRMoGA over several state-of-the-art baselines, e.g., up to 39.16% improvement regarding the Recall score on Foursquare.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Key Research and Development Project of Sichuan Province

National Science Foundation SWIFT

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

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