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 Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In graphs with rich texts, incorporating textual information with structuralinformation would benefit constructing expressive graph embeddings. Amongvarious graph embedding models, random walk (RW)-based is one of the mostpopular and successful groups. However, it is challenged by two issues whenapplied on graphs with rich texts: (i) sampling efficiency: deriving from thetraining objective of RW-based models (e.g., DeepWalk and node2vec), we showthat RW-based models are likely to generate large amounts of redundant trainingsamples due to three main drawbacks. (ii) text utilization: these models havedifficulty in dealing with zero-shot scenarios where graph embedding modelshave to infer graph structures directly from texts. To solve these problems, wepropose a novel framework, namely Text-driven Graph Embedding with PairsSampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the samplingstrategy of RW, being able to reduce ~99% training samples while preservingcompetitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), aninductive graph embedding approach, to generate node embeddings from texts.Since each node contains rich texts, TGE is able to generate high-qualityembeddings and provide reasonable predictions on existence of links to unseennodes. We evaluate TGE-PS on several real-world datasets, and experimentresults demonstrate that TGE-PS produces state-of-the-art results on bothtraditional and zero-shot link prediction tasks.

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework