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The proposed model adopts an autoencoder architecture, which utilizes graph attentional layers to capture the structural feature of neighborhood nodes, as well as a set of graph convolutional layers to capture motif features. A graph recurrent unit layer with self-attention is utilized to learn temporal variations in the snapshot sequence. We run comparative experiments on four realistic networks to validate the effectiveness of TSAM. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.<\/jats:p>","DOI":"10.3233\/ida-205524","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T19:44:50Z","timestamp":1642535090000},"page":"173-188","source":"Crossref","is-referenced-by-count":8,"title":["Temporal link prediction in directed networks based on self-attention mechanism"],"prefix":"10.1177","volume":"26","author":[{"given":"Jinsong","family":"Li","sequence":"first","affiliation":[]},{"given":"Jianhua","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Shuxin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lintianran","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/IDA-205524_ref1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0378-8733(03)00009-1","article-title":"Friends and neighbors on the Web","volume":"25","author":"Adamic","year":"2003","journal-title":"Soc. 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