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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Graph comparison is a fundamental operation in data mining and informationretrieval. Due to the combinatorial nature of graphs, it is hard to balance theexpressiveness of the similarity measure and its scalability. Spectral analysisprovides quintessential tools for studying the multi-scale structure of graphsand is a well-suited foundation for reasoning about differences between graphs.However, computing full spectrum of large graphs is computationallyprohibitive; thus, spectral graph comparison methods often rely on roughapproximation techniques with weak error guarantees. In this work, we proposeSLaQ, an efficient and effective approximation technique for computing spectraldistances between graphs with billions of nodes and edges. We derive thecorresponding error bounds and demonstrate that accurate computation ispossible in time linear in the number of graph edges. In a thoroughexperimental evaluation, we show that SLaQ outperforms existing methods,oftentimes by several orders of magnitude in approximation accuracy, andmaintains comparable performance, allowing to compare million-scale graphs in amatter of minutes on a single machine.

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs