HIGHLIGHTS
- who: Liliana Martirano from the Department of Computer (DIMES), of Calabria, Rende, Italy have published the research work: Co-MLHAN: contrastive learning for multilayer heterogeneous attributed networks, in the Journal: (JOURNAL)
- what: The authors propose a novel framework named Co-MLHAN to learn node embeddings for networks that are simultaneously multilayer heterogeneous and attributed. The authors evaluate the framework on the entity classification task. To fill the above gap in the literature, in this work the authors propose a novel Contrastive learning based framework for Multilayer Heterogeneous Attributed Networks (Co-MLHAN), which is designed to . . .
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