2024/02/25 by Nicola Cotumaccio, Cotumaccio, Nicola
Computer Science · #Advanced Graph Neural Networks #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Graph Theory and Algorithms #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2402.16205
openalex publication_date 2024/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Pattern matching queries on strings can be solved in linear time by Knuth-Morris-Pratt (KMP) algorithm. In 1973, Weiner introduced the suffix tree of a string [FOCS 1973] and showed that the seemingly more difficult problem of computing matching statistics can also be solved in liner time. Pattern matching queries on graphs are inherently more difficult: under the Orthogonal Vector hypothesis, the graph pattern matching problem cannot be solved in subquadratic time [TALG 2023]. The complexity of graph pattern matching can be parameterized by the topological complexity of the considered graph, which is captured by a parameter p [JACM 2023]. In this paper, we show that, as in the string setting, computing matching statistics on graph is as difficult as solving standard pattern matching queries. To this end, we introduce a notion of longest common prefix (LCP) array for arbitrary graphs.