  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.
