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

Networked representations of real-world phenomena are often partiallyobserved, which lead to incomplete networks. Analysis of such incompletenetworks can lead to skewed results. We examine the following problem: given anincomplete network, which $b$ nodes should be probed to bring the largestnumber of new nodes into the observed network? Many graph-mining tasks requirehaving observed a considerable amount of the network. Examples includecommunity discovery, belief propagation, influence maximization, etc. Forinstance, consider someone who has observed a portion (say 1%) of the Twitterretweet network via random tweet sampling. She wants to estimate the size ofthe largest connected component of the fully observed retweet network. Toimprove her estimate, how should she use her limited budget to reduce theincompleteness of the network? In this work, we propose a novel algorithm,called MaxOutProbe, which uses a budget $b$ (on nodes probed) to increase thesize of the observed network in terms of the number of nodes. Our experiments,across a range of datasets and conditions, demonstrate the advantages ofMaxOutProbe over existing methods.

MaxOutProbe: An Algorithm for Increasing the Size of Partially Observed Networks