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Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network

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Yong LiAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 122 - 132

https://doi.org/10.1145/3543507.3583239

Published: 30 April 2023 Publication History

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Abstract

Socioeconomic indicators reflect location status from various aspects such as demographics, economy, crime and land usage, which play an important role in the understanding of location-based social networks (LBSNs). Especially, several existing works leverage multi-source data for socioeconomic indicator prediction in LBSNs, which however fail to capture semantic information as well as distil comprehensive knowledge therein. On the other hand, knowledge graph (KG), which distils semantic knowledge from multi-source data, has been popular in recent LBSN research, which inspires us to introduce KG for socioeconomic indicator prediction in LBSNs. Specifically, we first construct a location-based KG (LBKG) to integrate various kinds of knowledge from heterogeneous LBSN data, including locations and other related elements like point of interests (POIs), business areas as well as various relationships between them, such as spatial proximity and functional similarity. Then we propose a hierarchical KG learning model to capture both global knowledge from LBKG and domain knowledge from several sub-KGs. Extensive experiments on three datasets demonstrate our model’s superiority over state-of-the-art methods in socioeconomic indicators prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KG-socioeconomic-indicator-prediction.

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Yu XZhou CFang YZhang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645423(515-526)Online publication date: 13-May-2024
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Gao JPeng PLu FWang SXie XClaramunt C(2024)Knowledge‐driven spatial competitive intelligence for tourismTransactions in GIS10.1111/tgis.1314528:3(535-563)Online publication date: 25-Feb-2024
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Index Terms

Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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WWW '23: The ACM Web Conference 2023

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Cited By

Cao LDeng HYang YWang CChen LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale GraphProceedings of the ACM Web Conference 202410.1145/3589334.3645452(570-581)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645452
Yu XZhou CFang YZhang XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsProceedings of the ACM Web Conference 202410.1145/3589334.3645423(515-526)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645423
Gao JPeng PLu FWang SXie XClaramunt C(2024)Knowledge‐driven spatial competitive intelligence for tourismTransactions in GIS10.1111/tgis.1314528:3(535-563)Online publication date: 25-Feb-2024
https://doi.org/10.1111/tgis.13145
Ning YLiu HWang HZeng ZXiong HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)UUKGProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668849(62442-62456)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668849
Liu YZhou ZLi YJin D(2023)Urban Knowledge Graph Aided Mobile User ProfilingACM Transactions on Knowledge Discovery from Data10.1145/359660418:1(1-30)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3596604
Zhou ZDing JLiu YJin DLi YDamiani MRenz MEldawy AKröger PNascimento M(2023)Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising DiffusionProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625641(1-12)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625641
Liu YDing JFu YLi Y(2023)UrbanKG: An Urban Knowledge Graph SystemACM Transactions on Intelligent Systems and Technology10.1145/358857714:4(1-25)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3588577
Zhang SLi THui SLi GLiang YYu LJin DLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge GraphProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599801(4842-4851)Online publication date: 6-Aug-2023
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