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 learns low-dimensional vector representation fornodes in the network, has attracted considerable research attention recently.However, the existing methods are incapable of handling billion-scale networks,because they are computationally expensive and, at the same time, difficult tobe accelerated by distributed computing schemes. To address these problems, wepropose RandNE (Iterative Random Projection Network Embedding), a novel andsimple billion-scale network embedding method. Specifically, we propose aGaussian random projection approach to map the network into a low-dimensionalembedding space while preserving the high-order proximities between nodes. Toreduce the time complexity, we design an iterative projection procedure toavoid the explicit calculation of the high-order proximities. Theoreticalanalysis shows that our method is extremely efficient, and friendly todistributed computing schemes without any communication cost in thecalculation. We also design a dynamic updating procedure which can efficientlyincorporate the dynamic changes of the networks without error aggregation.Extensive experimental results demonstrate the efficiency and efficacy ofRandNE over state-of-the-art methods in several tasks including networkreconstruction, link prediction and node classification on multiple datasetswith different scales, ranging from thousands to billions of nodes and edges.

Billion-scale Network Embedding with Iterative Random Projection