English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Invertible transformation of large graphs into fixed dimensional vectors(embeddings) remains a challenge. Its overcoming would reduce any operation ongraphs to an operation in a vector space. However, most existing methods arelimited to graphs with tens of vertices. In this paper we address the abovechallenge with recursive neural networks - the encoder and the decoder. Theencoder network transforms embeddings of subgraphs into embeddings of largersubgraphs, and eventually into the embedding of the input graph. The decoderdoes the opposite. The dimension of the embeddings is constant regardless ofthe size of the (sub)graphs. Simulation experiments presented in this paperconfirm that our proposed graph autoencoder, ReGAE, can handle graphs with eventhousands of vertices.

ReGAE: Graph autoencoder based on recursive neural networks