2017/04/24 by Vincenzo Carletti, Pasquale Foggia, Alessia Saggese +1 · 2 citations
Computer Science · Mathematics · #Graph Theory and Algorithms #Advanced Graph Neural Networks #Complexity and Algorithms in Graphs #Subgraph isomorphism problem #Induced subgraph isomorphism problem #Graph isomorphism #Computer science #Matching (statistics) #Theoretical computer science #Random graph #Isomorphism (crystallography) #Time complexity #Indifference graph #Pathwidth #Chordal graph #Graph #Algorithm #Mathematics #Line graph
paper · doi:10.1109/tpami.2017.2696940
openalex publication_date 2017/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Graph matching is essential in several fields that use structured information, such as biology, chemistry, social networks, knowledge management, document analysis and others. Except for special classes of graphs, graph matching has in the worst-case an exponential complexity; however, there are algorithms that show an acceptable execution time, as long as the graphs are not too large and not too dense. In this paper we introduce a novel subgraph isomorphism algorithm, VF3, particularly efficient in the challenging case of graphs with thousands of nodes and a high edge density. Its performance, both in terms of time and memory, has been assessed on a large dataset of 12,700 random graphs with a size up to 10,000 nodes, made publicly available. VF3 has been compared with four other state-of-the-art algorithms, and the huge experimentation required more than two years of processing time. The results confirm that VF3 definitely outperforms the other algorithms when the graphs become huge and dense, but also has a very good performance on smaller or sparser graphs.