Community Search (CS) aims to enable online and personalized discovery of communities. Recently, attention to the CS problem in directed graphs (di-graph) needs to be improved despite the extensive study conducted on undirected graphs. Nevertheless, the existing studies are plagued by several shortcomings, e.g., Achieving high-performance CS while ensuring the retrieved community is cohesive is challenging. This paper uses the D-truss model to address the limitations of investigating the CS problem in large di-graphs. We aim to implement millisecond-level D-truss CS in di-graphs by building a summarized graph index. To capture the interconnectedness of edges within D-truss communities, we propose an innovative equivalence relation known as D-truss-equivalence, which allows us to divide the edges in a di-graph into a sequence of super nodes (s-nodes). These s-nodes form the D-truss-equivalencebased index, DEBI, an index structure that preserves the truss properties and ensures efficient space utilization. Using DEBI, CS can be performed without time-consuming access to the original graph. The experiments indicate that our method can achieve millisecond-level D-truss community query while ensuring high community quality. In addition, dynamic maintenance of indexes can also be achieved at a lower cost. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XieCanhao04/DEBI</uri> .
No takes yet. Share an insight, caveat, or question.
Ai et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: