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 compression is a data analysis technique that consists in thereplacement of parts of a graph by more general structural patterns in order toreduce its description length. It notably provides interesting explorationtools for the study of real, large-scale, and complex graphs which cannot begrasped at first glance. This article proposes a framework for the compressionof temporal graphs, that is for the compression of graphs that evolve withtime. This framework first builds on a simple and limited scheme, exploitingstructural equivalence for the lossless compression of static graphs, thengeneralises it to the lossy compression of link streams, a recent formalism forthe study of temporal graphs. Such generalisation relies on the naturalextension of (bidimensional) relational data by the addition of a thirdtemporal dimension. Moreover, we introduce an information-theoretic measure toquantify and to control the information that is lost during compression, aswell as an algebraic characterisation of the space of possible compressionpatterns to enhance the expressiveness of the initial compression scheme. Thesecontributions lead to the definition of a combinatorial optimisation problem,that is the Lossy Multistream Compression Problem, for which we provide anexact algorithm.

An Information-theoretic Framework for the Lossy Compression of Link Streams