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- W4386472901 abstract "With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (eg chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Top-k Edge-Diversified Patterns Discovery problem</i> to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$textsc {Ted}^+$</tex-math></inline-formula> which achieves a guaranteed approximation ratio to the optimal result. Three optimization strategies are further developed to improve the performance, and a lightweight version called <sc xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>TedLite</small> is designed for even larger graph databases. Experimental studies on real-world datasets demonstrate the superiority of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$textsc {Ted}^+$</tex-math></inline-formula> to traditional techniques." @default.
- W4386472901 created "2023-09-07" @default.
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- W4386472901 date "2023-01-01" @default.
- W4386472901 modified "2023-10-02" @default.
- W4386472901 title "TED$^+$: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database" @default.
- W4386472901 doi "https://doi.org/10.1109/tkde.2023.3312566" @default.
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