Exploring multi-relational spatial interaction imputation with distance-decay effects

ABSTRACT

Spatial interaction imputation aims to compensate for missing stable connections in geographical space, bolstering interaction network integrity and accuracy. Graph neural networks excel in graph-structured interaction data. However, existing research often focuses on homogeneous networks, neglecting the impact of heterogeneous interaction relationships influenced by distance decay on interaction imputation. Neglecting edge heterogeneity constrains the ability to effectively model the network structure, consequently leading to suboptimal performance in interaction imputation. This study introduces an interaction imputation graph convolutional network model. It constructs a heterogeneous interaction network with multi-distance relationships, considering distance decay. The model performs graph embedding based on interaction relationships between nodes. It comprehensively incorporates multiple interaction modes, topological structures, and node attributes to enhance spatial interaction imputation accuracy. Empirically validated using Beijing taxi travel data, our model outperforms existing models, improving imputation accuracy by approximately 8.70%. Our model consistently maintains superior accuracy in interaction networks of various sizes, demonstrating the stable superiority of our model. We also demonstrated that the variation in the number of interaction relationships affects imputation accuracy. A reasonable number of relationships and a larger feature dimension of geographical units yield better interaction imputation results.

KEYWORDS:

• Spatial interaction network
• graph embedding
• heterogeneous networks
• distance-decay effect
• interaction imputation

Acknowledgement

The authors grateful to all the reviewers and editors for their constructive suggestion that improve the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The dataset that support the findings of this study are available with the identifier at the https://doi.org/10.6084/m9.figshare.23884284.v1

Additional information

Funding

This research was supported by the National Nature Science Foundation of China (Grant number No. 42071376); this research was funded by the Open Project Program of the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (grant number 01122220010028).