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 has recently emerged as a promising technique to embednodes of a network into low-dimensional vectors. While fairly successful, mostexisting works focus on the embedding techniques for static networks. But inpractice, there are many networks that are evolving over time and hence aredynamic, e.g., the social networks. To address this issue, a high-orderspatio-temporal embedding model is developed to track the evolutions of dynamicnetworks. Specifically, an activeness-aware neighborhood embedding method isfirst proposed to extract the high-order neighborhood information at each giventimestamp. Then, an embedding prediction framework is further developed tocapture the temporal correlations, in which the attention mechanism is employedinstead of recurrent neural networks (RNNs) for its efficiency in computing andflexibility in modeling. Extensive experiments are conducted on four real-worlddatasets from three different areas. It is shown that the proposed methodoutperforms all the baselines by a substantial margin for the tasks of dynamiclink prediction and node classification, which demonstrates the effectivenessof the proposed methods on tracking the evolutions of dynamic networks.

Embedding Dynamic Attributed Networks by Modeling the Evolution Processes