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Lorenzo Arsini, Barbara Caccia, Andrea Ciardiello, Stefano Giagu and Carlo Mancini Terracciano
Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs st...
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Hongmei Tang, Wenzhong Tang, Ruichen Li, Yanyang Wang, Shuai Wang and Lihong Wang
Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning div...
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Lili Sun, Xueyan Liu, Min Zhao and Bo Yang
Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data. However, most existing methods based on v...
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