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/

Zendy Plus

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

Zendy Tools

Zendy Open

Estimating the frequency of sub-graphs is of importance for many tasks,including sub-graph isomorphism, kernel-based anomaly detection, and networkstructure analysis. While multiple algorithms were proposed for fullenumeration or sampling-based estimates, these methods fail in very largegraphs. Recent advances in parallelization allow for estimates of totalsub-graphs counts in very large graphs. The task of counting the frequency ofeach sub-graph associated with each vertex also received excellent solutionsfor undirected graphs. However, there is currently no good solution for verylarge directed graphs.  We here propose VDMC (Vertex specific Distributed Motif Counting) -- a fullydistributed algorithm to optimally count all the 3 and 4 vertices connecteddirected graphs (sub-graph motifs) associated with each vertex of a graph. VDMCcounts each motif only once and its efficacy is linear in the number of countedmotifs. It is fully parallelized to be efficient in GPU-based computation. VDMCis based on three main elements: 1) Ordering the vertices and only countingmotifs containing increasing order vertices, 2) sub-ordering motifs based onthe average length of the BFS composing the motif, and 3) removing isomorphismsonly once for the entire graph. We here compare VDMC to analytical estimates ofthe expected number of motifs and show its accuracy. VDMC is available as ahighly efficient CPU and GPU code with a novel data structure for efficientgraph manipulation. We show the efficacy of VDMC and real-world graphs. VDMCallows for the precise analysis of sub-graph frequency around each vertex inlarge graphs and opens the way for the extension of methods until now limitedto graphs of thousands of edges to graphs with millions of edges and above.  GIT: https://github.com/louzounlab/graph-measures

BFS based distributed algorithm for parallel local directed sub-graph enumeration