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

In many advanced network analysis applications, like social networks,e-commerce, and network security, hotspots are generally considered as a groupof vertices that are tightly connected owing to the similar characteristics,such as common habits and location proximity. In this paper, we investigate theformation of hotspots from an alternative perspective that considers the routesalong the network paths as the auxiliary information, and attempt to find theroute hotspots in large labeled networks. A route hotspot is a cohesivesubgraph that is covered by a set of routes, and these routes correspond to thesame sequential pattern consisting of vertices' labels. To the best of ourknowledge, the problem of Finding Route Hotspots in Large Labeled Networks hasnot been tackled in the literature. However, it is challenging as counting thenumber of hotspots in a network is #P-hard. Inspired by the observation thatthe sizes of hotspots decrease with the increasing lengths of patterns, weprove several anti-monotonicity properties of hotspots, and then develop ascalable algorithm called FastRH that can use these properties to effectivelyprune the patterns that cannot form any hotspots. In addition, to avoid theduplicate computation overhead, we judiciously design an effective indexstructure called RH-Index for storing the hotspot and pattern informationcollectively, which also enables incremental updating and efficient queryprocessing. Our experimental results on real-world datasets clearly demonstratethe effectiveness and scalability of our proposed methods.