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However, the existing methods consider only few aspects of networks at a time. In this paper, we propose a novel framework, named , to learn node embeddings for networks that are simultaneously <jats:italic>multilayer<\/jats:italic>, <jats:italic>heterogeneous<\/jats:italic> and <jats:italic>attributed<\/jats:italic>. We leverage <jats:italic>contrastive learning<\/jats:italic> as a self-supervised and task-independent machine learning paradigm and define a cross-view mechanism between two views of the original graph which collaboratively supervise each other. We evaluate our framework on the entity classification task. Experimental results demonstrate the effectiveness of  and its variant , showing their capability of exploiting across-layer information in addition to other types of knowledge.<\/jats:p>","DOI":"10.1007\/s41109-022-00504-9","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T11:04:09Z","timestamp":1663671849000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Co-MLHAN: contrastive learning for multilayer heterogeneous attributed networks"],"prefix":"10.1007","volume":"7","author":[{"given":"Liliana","family":"Martirano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Zangari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Tagarelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"key":"504_CR1","doi-asserted-by":"crossref","unstructured":"Ahrabian K, Feizi A, Salehi Y, Hamilton WL, Bose AJ (2020) Structure aware negative sampling in knowledge graphs. 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