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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Subgraph isomorphism or subgraph matching is generally considered as anNP-complete problem, made more complex in practical applications where the edgeweights take real values and are subject to measurement noise and possibleanomalies. To the best of our knowledge, almost all subgraph matching methodsutilize node labels to perform node-node matching. In the absence of suchlabels (in applications such as image matching and map matching among others),these subgraph matching methods do not work. We propose a method foridentifying the node correspondence between a subgraph and a full graph in theinexact case without node labels in two steps - (a) extract the minimal uniquetopology preserving subset from the subgraph and find its feasible matching inthe full graph, and (b) implement a consensus-based algorithm to expand thematched node set by pairing unique paths based on boundary commutativity. Goingbeyond the existing subgraph matching approaches, the proposed method is shownto have realistically sub-linear computational efficiency, robustness to randommeasurement noise, and good statistical properties. Our method is also readilyapplicable to the exact matching case without loss of generality. Todemonstrate the effectiveness of the proposed method, a simulation and a casestudy is performed on the Erdos-Renyi random graphs and the image-based affinecovariant features dataset respectively.

Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology