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

Structural entropy is a metric that measures the amount of informationembedded in graph structure data under a strategy of hierarchical abstracting.To measure the structural entropy of a dynamic graph, we need to decode theoptimal encoding tree corresponding to the optimal hierarchical communitypartitioning of the graph. However, the current structural entropy methods donot support efficient incremental updating of encoding trees. To address thisissue, we propose Incre-2dSE, a novel incremental measurement framework thatdynamically adjusts the community partitioning and efficiently computes theupdated structural entropy for each snapshot of dynamic graphs. Incre-2dSEconsists of an online module and an offline module. The online module includesdynamic measurement algorithms based on two dynamic adjustment strategies fortwo-dimensional encoding trees, i.e., the naive adjustment strategy and thenode-shifting adjustment strategy, which supports theoretical analysis of theupdated structural entropy and incrementally adjusts the community partitioningtowards a lower structural entropy. In contrast, the offline module globallyconstructs the encoding tree for the updated graph using static communitydetection methods and calculates the structural entropy by definition. Weconduct experiments on an artificial dynamic graph dataset generated by HawkesProcess and 3 real-world datasets. Experimental results confirm that ourdynamic measurement algorithms effectively capture the dynamic evolution of thecommunities, reduce time consumption, and provide great interpretability.

Incremental Measurement of Structural Entropy for Dynamic Graphs