Tackling higher-order relations and heterogeneity: Dynamic heterogeneous hypergraph network for spatiotemporal...

Area of Science:

• Computer Science
• Artificial Intelligence
• Data Science

Background:

• Spatiotemporal activity prediction is crucial for urban planning and recommendation systems.
• Existing graph-based methods struggle to model complex, higher-order relationships and heterogeneity in user activities.
• Static graph structures relying on co-occurrence are often imprecise for dynamic activity patterns.

Purpose of the Study:

• To propose a novel dynamic heterogeneous hypergraph network (DyH²N) for improved spatiotemporal activity prediction.
• To address the limitations of existing methods in modeling higher-order relations and data heterogeneity.
• To develop a model capable of dynamically updating its structure for more accurate predictions.

Main Methods:

• Utilizing hypergraphs instead of traditional graphs to capture higher-order relations.
• Introducing a heterogeneous hyperedge learning module inspired by set representation learning for non-decomposable modeling.
• Incorporating a knowledge representation-regularized loss for enhanced hyperedge encoding.
• Implementing a hypergraph structure learning module for dynamic structure updates.

Main Results:

• DyH²N significantly outperforms state-of-the-art methods on four real-world datasets, with performance gains ranging from 5.98% to 27.13%.
• Ablation experiments confirm the effectiveness of individual components within the DyH²N framework.
• The model demonstrates superior ability in capturing intricate spatiotemporal activity patterns.

Conclusions:

• DyH²N provides a powerful new approach for spatiotemporal activity prediction by effectively modeling higher-order relations and heterogeneity.
• The dynamic and heterogeneous nature of the proposed hypergraph network leads to substantial performance improvements.
• This research offers a promising direction for future advancements in activity prediction and related applications.