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

Streaming graph processing involves performing updates and analytics on atime-evolving graph. The underlying representation format largely determinesthe throughputs of these updates and analytics phases. Existing formats usuallyemploy variations of hash tables or adjacency lists. However,adjacency-list-based approaches perform poorly on heavy-tailed graphs, and thehash-based approaches suffer on short-tailed graphs. We propose GraphTango, ahybrid format that provides excellent update and analytics throughputregardless of the graph's degree distribution. GraphTango switches among threedifferent formats based on a vertex's degree: i) Low-degree vertices store theedges directly with the neighborhood metadata, confining accesses to a singlecache line, ii) Medium-degree vertices use adjacency lists, and iii)High-degree vertices use hash tables as well as adjacency lists. In this case,adjacency list provides fast traversal during the analytics phase, while thehash table provides constant-time lookups during the update phase. We furtheroptimized the performance by designing an open-addressing-based hash table thatfully utilizes every fetched cache line. In addition, we developed athread-local lock-free memory pool that allows fast growing/shrinking of theadjacency lists and hash tables in a multi-threaded environment. We evaluatedGraphTango with the help of the SAGA-Bench framework and compared it with fourother representation formats. On average, GraphTango provides 4.5x higherinsertion throughput, 3.2x higher deletion throughput, and 1.1x higheranalytics throughput over the next best format. Furthermore, we integratedGraphTango with the state-of-the-art graph processing frameworks DZiG andRisGraph. Compared to the vanilla DZiG and vanilla RisGraph, [GraphTango +DZiG] and [GraphTango + RisGraph] reduces the average batch processing time by2.3x and 1.5x, respectively.

GraphTango: A Hybrid Representation Format for Efficient Streaming Graph Updates and Analysis