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A Novel Prediction Model for Trajectory-User Linking Based on Graph Attention Network

Hai‐Tao Zhang et al.

International Journal of Data Warehousing and Mining2026https://doi.org/10.4018/ijdwm.406758article
ABDC C
Weight
0.50

What the paper says

Trajectory-user linking (TUL) plays a vital role in multisource geospatial data analytics for behavioral pattern recognition and user identification. Addressing the limitations of conventional TUL approaches in computational efficiency and predictive accuracy, this study proposes a novel prediction model for TUL based on graph attention network (PMTULGAN) that harnesses graph attention networks to significantly enhance predictive performance of TUL tasks. PMTULGAN's key innovation lies in its dynamic attention mechanism, which adaptively allocates weights to nodes to facilitate more accurate extraction and interpretation of salient features within complex trajectory data. Extensive experimental evaluations reveal a substantial performance enhancement for the proposed method over conventional methods. These empirical results underscore the robustness and reliability of PMTULGAN in various data scenarios and substantiate its practical utility in real-world applications.

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https://doi.org/https://doi.org/10.4018/ijdwm.406758

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@article{hai‐tao2026,
  title        = {{A Novel Prediction Model for Trajectory-User Linking Based on Graph Attention Network}},
  author       = {Hai‐Tao Zhang et al.},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.406758},
}

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A Novel Prediction Model for Trajectory-User Linking Based on Graph Attention Network

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.