Probabilistic Graph Pattern Matching via Tumor Knowledge Graph
Lei Li, Houdie Tu, Zhenchao Tao, Chenyang Bu, Xindong Wu · ACM transactions on probabilistic machine learning. · 2024
Graph pattern matching (GPM) entails the identification of subgraphs within a larger graph structure that either precisely mirror or closely parallel a predefined pattern graph. Despite the fact that research on GPM in large-scale graph data has been largely centered on social network analysis or enhancing the precision and efficiency of matching algorithms for expeditious subgraph retrieval, there is a noticeable absence of studies committed to probing GPM in medical domains. To rectify this shortcoming and probe the potential of GPM in clinical contexts, particularly in aiding patients with the selection of optimal tumor treatment plans, this article introduces the concept of probabilistic GPM specifically modified for the tumor knowledge graph (TKG). We propose a multi-constraint GPM algorithm, hereinafter designated as TKG-McGPM, customized for the TKG. Through experimental verification, we establish that TKG-McGPM can facilitate more efficient and informed decision-making in tumor treatment planning.