# Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)

arXiv: https://arxiv.org/abs/2108.03400 · HTML: https://mlsanity.com/paper/2108.03400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks](https://mlsanity.com/paper/1711.08742/) (2017)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [PageRank beyond the Web](https://mlsanity.com/paper/1407.5107/) (2014)
- [Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks](https://mlsanity.com/paper/1705.02699/) (2017)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN](https://mlsanity.com/paper/2004.01024/) (2020)
- [A Survey on Dynamic Network Embedding](https://mlsanity.com/paper/2006.08093/) (2020)
- [Eigenvector-Based Centrality Measures for Temporal Networks](https://mlsanity.com/paper/1507.01266/) (2015)
- [Node-Centric Graph Learning from Data for Brain State Identification](https://mlsanity.com/paper/2011.02179/) (2020)
- [Case studies in network community detection](https://mlsanity.com/paper/1705.02305/) (2017)
- [Estimation of Missing Data in Intelligent Transportation System](https://mlsanity.com/paper/2101.03295/) (2021)

## Abstract (arXiv)

GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time with their edge may disappear, or node/edge attribute may alter from one time to the other. It is essential to consider such evolution in representation learning of nodes in time varying graphs. In this paper, we propose a Temporal Multi-layered Position-aware Graph Neural Network (TMP-GNN), a node embedding approach for dynamic graph that incorporates the interdependence of temporal relations into embedding computation. We evaluate the performance of TMP-GNN on two different representations of temporal multilayered graphs. The performance is assessed against the most popular GNNs on node-level prediction tasks. Then, we incorporate TMP-GNN into a deep learning framework to estimate missing data and compare the performance with their corresponding competent GNNs from our former experiment, and a baseline method. Experimental results on four real-world datasets yield up to 58% of lower ROC AUC for pairwise node classification task, and 96% of lower MAE in missing feature estimation, particularly for graphs with a relatively high number of nodes and lower mean degree of connectivity.