MoHL：用超图拉普拉斯混合模型从不完整时空数据中学习高阶交互

View PDF HTML (experimental)

Abstract:Traffic sensor data exhibit strong temporal and cross-sensor dependencies, yet long gaps erase both a sensor's local history and the joint observations needed to learn its relationships with other sensors. We introduce MoHL, a mixture of hypergraph Laplacians that learns group-wise relational structure directly from incomplete spatiotemporal data. MoHL discovers overlapping sensor groups from observed co-variation, constructs a Laplacian component for each interaction order, and learns their mixture through holdouts that mimic the missingness pattern. By sharing weights across group-induced couplings, MoHL pools scattered evidence instead of estimating each coupling independently. We characterize the induced weight-sharing structure and analyze the conditions under which sharing reduces estimation risk. Experiments on three traffic networks show that higher-order groups are most beneficial under long gaps, while support-matched comparisons demonstrate the advantage of shared weights when relational evidence is scarce. MoHL achieves the lowest error among the evaluated baseline methods under day-long gaps, with orders of magnitude fewer learned parameters than deep imputers. These results show how group-wise sharing can sustain reliable reconstruction precisely when missingness depletes the evidence needed to learn cross-sensor dependence.

Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2605.17316 [cs.LG]
	(or arXiv:2605.17316v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2605.17316 arXiv-issued DOI via DataCite

Submission history

From: Keshu Wu [view email]
[v1] Sun, 17 May 2026 08:15:16 UTC (649 KB)
[v2] Sun, 11 Oct 2026 02:28:48 UTC (4,751 KB)