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

This work bridges the gap between distributed and centralised models ofcomputing in the context of sublinear-time graph algorithms. A priori, typicalcentralised models of computing (e.g., parallel decision trees or centralisedlocal algorithms) seem to be much more powerful than distributedmessage-passing algorithms: centralised algorithms can directly probe any partof the input, while in distributed algorithms nodes can only communicate withtheir immediate neighbours. We show that for a large class of graph problems,this extra freedom does not help centralised algorithms at all: for example,efficient stateless deterministic centralised local algorithms can be simulatedwith efficient distributed message-passing algorithms. In particular, thisenables us to transfer existing lower bound results from distributed algorithmsto centralised local algorithms.

Non-Local Probes Do Not Help with Graph Problems