{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:27:04Z","timestamp":1772180824764,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"A.L.-G.\u2019s University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>GNNs have been proven to perform highly effectively 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 and their edge may disappear, or the node\/edge attribute may alter from one time to the other. It is essential to consider such evolution in the representation learning of nodes in time-varying graphs. In this paper, we propose a Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN), a node embedding approach for dynamic graphs 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 a node-level prediction task. 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% lower ROCAUC for the pair-wise node classification task, and 96% lower MAE in missing feature estimation, particularly for graphs with a relatively high number of nodes and lower mean degree of connectivity.<\/jats:p>","DOI":"10.3390\/make4020017","type":"journal-article","created":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T06:23:08Z","timestamp":1651386188000},"page":"397-417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1030-592X","authenticated-orcid":false,"given":"Bahareh","family":"Najafi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Toronto, BA4120, 40 St. George Street, Toronto, ON M5S 3G4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0865-8179","authenticated-orcid":false,"given":"Saeedeh","family":"Parsaeefard","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Toronto, BA4120, 40 St. George Street, Toronto, ON M5S 3G4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9888-0389","authenticated-orcid":false,"given":"Alberto","family":"Leon-Garcia","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Toronto, BA4120, 40 St. George Street, Toronto, ON M5S 3G4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"ref_1","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"Lv","year":"2014","journal-title":"IEEE Trans. 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