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Expressive top-k matching for conditional graph patterns

  • S.I. : Emerging trends in AI & ML
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Abstract

We propose conditional graph patterns (CGPs) that make conventional patterns more expressive, especially with positive and negative predicates. In emerging applications, CGPs allow to express complex search conditions and to find more sensible information than their traditional counterparts. We show that this expressivity does not come with a much higher price. Indeed, we propose a (parallel) matching algorithm that allows to match CGPs over any data graphs in quadratic time, as opposed to the prohibitive solutions based on subgraph isomorphism. In the second part of this article, we study the problem of top-k CGP matching algorithm. We propose the notion of relevance schema that allows users to define relevance criteria according to their preferences. We propose an early termination algorithm that finds the top-k relevant matches by requiring only \(3\%\) of the time spent by the naive algorithm. To our knowledge, this is the first effort that investigates an expressive top-k graph pattern matching under simulation semantic. An extensive experimental study has been conducted to prove effectiveness and efficiency of our results.

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Notes

  1. Indices are used to distinguish between pattern nodes of the same label.

  2. Except our work [28, 30] that deals with CQs only.

  3. Polynomial Time.

  4. Contrary to existing approaches that consider a static definition of relevance.

  5. This article extends [29] by including contributions (5–9).

  6. This is why we call them conditional graph patterns.

  7. This may not reduce the practicability of our approach since many query languages (e.g., XPath, SQL) adopt this syntax of predicates.

  8. One can prove this result by leveraging that of dual simulation [25].

  9. \(M(v,u,u') {:}{=} \{v'\in V\setminus (v,v')\in E,(u',v')\in S,{\mathcal {L}}(v,v')={\mathcal {L}}_{_q}(u,u'),{\mathcal {A}}(e_v)\sim {\mathcal {L}}_{_q}(e_u)\}\).

  10. The proof can be done by leveraging our results in [30].

  11. http://snap.stanford.edu/data/index.html.

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  1. Abou-Bekr Belkaid University, Tlemcen, Algeria

    Houari Mahfoud

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Mahfoud, H. Expressive top-k matching for conditional graph patterns. Neural Comput & Applic 34, 14205–14221 (2022). https://doi.org/10.1007/s00521-021-06590-7

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