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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Network embedding, which maps graphs to distributed representations, is aunified framework for various graph inference tasks. According to the topologyproperties (e.g., structural roles and community memberships of nodes) to bepreserved, it can be categorized into the identity and position embedding.However, existing methods can only capture one type of property. Someapproaches can support the inductive inference that generalizes the embeddingmodel to new nodes or graphs but relies on the availability of attributes. Dueto the complicated correlations between topology and attributes, it is unclearfor some inductive methods which type of property they can capture. In thisstudy, we explore a unified framework for the joint inductive inference ofidentity and position embeddings without attributes. An inductive random walkembedding (IRWE) method is proposed, which combines multiple attention units tohandle the random walk on graph topology and simultaneously derives identityand position embeddings that are jointly optimized. In particular, wedemonstrate that some random walk statistics can be informative features tocharacterize node identities and positions while supporting the inductiveembedding inference. Experiments validate the superior performance of IRWEbeyond various baselines for the transductive and inductive inference ofidentity and position embeddings.

IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding