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ORIGINAL RESEARCH article

Front. Big Data
Sec. Data Mining and Management
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1546850

Edge-level Multi-constranint Graph Pattern Matching with Lung Cancer Knowledge Graph

Provisionally accepted
Houdie Tu Houdie Tu 1Lei Li Lei Li 1*Zhenchao Tao Zhenchao Tao 2Zan Zhang Zan Zhang 1
  • 1 Hefei University of Technology, Hefei, China
  • 2 University of Science and Technology of China, Hefei, Anhui Province, China

The final, formatted version of the article will be published soon.

    Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research. In order to solve overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multiconstraint graph pattern matching algorithm TEM with lung cancer knowledge graph. This method effectively solves addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency. Furthermore, we apply Monte Carlo method on to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph. The experiments have verified the effectiveness and efficiency of TEM algorithm.

    Keywords: Graph pattern matching, Probability graph, Lung Cancer Knowledge Graph, Monte Carlo method, Multi-constranint

    Received: 17 Dec 2024; Accepted: 24 Jan 2025.

    Copyright: © 2025 Tu, Li, Tao and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Lei Li, Hefei University of Technology, Hefei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.