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

Graph sparsification is a technique that approximates a given graph by asparse graph with a subset of vertices and/or edges. The goal of an effectivesparsification algorithm is to maintain specific graph properties relevant tothe downstream task while minimizing the graph's size. Graph algorithms oftensuffer from long execution time due to the irregularity and the largereal-world graph size. Graph sparsification can be applied to greatly reducethe run time of graph algorithms by substituting the full graph with a muchsmaller sparsified graph, without significantly degrading the output quality.However, the interaction between numerous sparsifiers and graph properties isnot widely explored, and the potential of graph sparsification is not fullyunderstood.  In this work, we cover 16 widely-used graph metrics, 12 representative graphsparsification algorithms, and 14 real-world input graphs spanning variouscategories, exhibiting diverse characteristics, sizes, and densities. Wedeveloped a framework to extensively assess the performance of thesesparsification algorithms against graph metrics, and provide insights to theresults. Our study shows that there is no one sparsifier that performs the bestin preserving all graph properties, e.g. sparsifiers that preservedistance-related graph properties (eccentricity) struggle to perform well onGraph Neural Networks (GNN). This paper presents a comprehensive experimentalstudy evaluating the performance of sparsification algorithms in preservingessential graph metrics. The insights inform future research in incorporatingmatching graph sparsification to graph algorithms to maximize benefits whileminimizing quality degradation. Furthermore, we provide a framework tofacilitate the future evaluation of evolving sparsification algorithms, graphmetrics, and ever-growing graph data.

Demystifying Graph Sparsification Algorithms in Graph Properties Preservation