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

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