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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Due to their ability to model meaningful higher order relations among a setof entities, higher order network models have emerged recently as a powerfulalternative for graph-based network models which are only capable of modelingbinary relationships. Message passing paradigm is still dominantly used tolearn representations even for higher order network models. While powerful,message passing can have disadvantages during inference, particularly when thehigher order connectivity information is missing or corrupted. To overcome suchlimitations, we propose Topo-MLP, a purely MLP-based simplicial neural networkalgorithm to learn the representation of elements in a simplicial complexwithout explicitly relying on message passing. Our framework utilizes a novelHigher Order Neighborhood Contrastive (HONC) loss which implicitly incorporatesthe simplicial structure into representation learning. Our proposed model'ssimplicity makes it faster during inference. Moreover, we show that our modelis robust when faced with missing or corrupted connectivity structure.

Topo-MLP : A Simplicial Network Without Message Passing