# Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

arXiv: https://arxiv.org/abs/1912.05693 · HTML: https://mlsanity.com/paper/1912.05693/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 30 of 30)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation](https://mlsanity.com/paper/2104.14936/) (2021)
- [Jointly Contrastive Representation Learning on Road Network and Trajectory](https://mlsanity.com/paper/2209.06389/) (2022)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach](https://mlsanity.com/paper/2210.11780/) (2022)
- [MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis](https://mlsanity.com/paper/2306.02831/) (2023)
- [Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation](https://mlsanity.com/paper/2107.00946/) (2021)
- [STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph](https://mlsanity.com/paper/2210.16789/) (2022)
- [Dynamic Causal Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2306.07019/) (2023)
- [Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation](https://mlsanity.com/paper/2103.08323/) (2021)
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://mlsanity.com/paper/2306.06994/) (2023)
- [Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction](https://mlsanity.com/paper/2101.06954/) (2021)
- [DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction](https://mlsanity.com/paper/2208.06794/) (2022)
- [Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting](https://mlsanity.com/paper/2306.09386/) (2023)
- [Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning](https://mlsanity.com/paper/2401.12681/) (2024)
- [Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling](https://mlsanity.com/paper/2404.04403/) (2024)
- [Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering](https://mlsanity.com/paper/2310.20224/) (2023)
- [A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery](https://mlsanity.com/paper/2311.02631/) (2023)
- [Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer](https://mlsanity.com/paper/2407.18554/) (2024)
- [Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data](https://mlsanity.com/paper/2108.03900/) (2021)
- [Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data](https://mlsanity.com/paper/2309.03439/) (2023)
- [Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network](https://mlsanity.com/paper/2110.02271/) (2021)
- [Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market](https://mlsanity.com/paper/2407.10878/) (2024)
- [Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians](https://mlsanity.com/paper/2605.17316/) (2026)
- [Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates](https://mlsanity.com/paper/2407.11180/) (2024)
- [Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture](https://mlsanity.com/paper/2407.21068/) (2024)
- [Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast](https://mlsanity.com/paper/2409.15200/) (2024)
- [Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference](https://mlsanity.com/paper/2503.09646/) (2025)
- [AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation](https://mlsanity.com/paper/2509.18144/) (2025)
- [Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition](https://mlsanity.com/paper/2510.00460/) (2025)

## Abstract (arXiv)

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the L1-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate improved performance over the regular tensor completion methods.