@inproceedings{50247895a97a4da3a4e14c8952b06485,
title = "Batch and negative sampling design for human mobility graph neural network training",
abstract = "This study presents a deep learning approach to human mobility prediction within a network science framework. We proposed a human mobility graph that defines two types of nodes - -people and locations - -and employs domain-specific attributes to capture the temporal and spatial dynamics of human movement. The graph is too large to fit into a single GPU, necessitating the design of a specialized mini-batching strategy that incorporates behavioral aspects. The study also introduces a location-aware negative sampling method to enhance the training process. The results achieved the top 10 accuracy based on GEO-BLEU and Dynamic Time Warping in HuMob Challenge 2023. It serves as a call to action for researchers in the field to consider the entire deep learning process in human mobility studies. Looking ahead, the study advocates for the release of more comprehensive datasets to further enrich our understanding of human mobility patterns.",
keywords = "graph neural network, heterogenous graph, human mobility, mini-batching, time series",
author = "Jiaxin Du and Xinyue Ye",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author(s).; 1st ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge, HuMob 2023 ; Conference date: 13-11-2023",
year = "2023",
month = nov,
day = "13",
doi = "10.1145/3615894.3628504",
language = "English (US)",
series = "HuMob 2023 - 1st ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge",
publisher = "Association for Computing Machinery, Inc",
pages = "47--50",
editor = "Takahiro Yabe and Kota Tsubouchi and Toru Shimizu and Yoshihide Sekimoto and Kaoru Sezaki and Esteban Moro and Pentland, {Alex Sandy}",
booktitle = "HuMob 2023 - 1st ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge",
}