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

Given a set of k networks, possibly with different sizes and no overlaps innodes or edges, how can we quickly assess similarity between them, withoutsolving the node-correspondence problem? Analogously, how can we extract asmall number of descriptive, numerical features from each graph thateffectively serve as the graph's "signature"? Having such features will enablea wealth of graph mining tasks, including clustering, outlier detection,visualization, etc.  We propose NetSimile -- a novel, effective, and scalable method for solvingthe aforementioned problem. NetSimile has the following desirable properties:(a) It gives similarity scores that are size-invariant. (b) It is scalable,being linear on the number of edges for "signature" vector extraction. (c) Itdoes not need to solve the node-correspondence problem. We present extensiveexperiments on numerous synthetic and real graphs from disparate domains, andshow NetSimile's superiority over baseline competitors. We also show howNetSimile enables several mining tasks such as clustering, visualization,discontinuity detection, network transfer learning, and re-identificationacross networks.

NetSimile: A Scalable Approach to Size-Independent Network Similarity