Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)
This addresses the problem of handling evolving graphs with missing data for applications in domains like social networks or recommendation systems, but it appears incremental as it builds on existing GNN methods.
The paper tackles missing data estimation in dynamic graphs by proposing TMP-GNN, which incorporates temporal relations into node embeddings, resulting in up to 58% lower ROC AUC for node classification and 96% lower MAE for missing feature estimation on real-world datasets.
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.