@inbook{1e50b8fa60544d7092298b5706575e8d,
title = "Motif Finding Algorithms: A Performance Comparison",
abstract = "Network motifs are subgraphs of a network that occur more frequently than expected, according to some reasonable null model. They represent building blocks of complex systems such as genetic interaction networks or social networks and may reveal intriguing typical but perhaps unexpected relationships between interacting entities. The identification of network motif is a time consuming task since it subsumes the subgraph matching problem. Several algorithms have been proposed in the literature. In this paper we aim to review the motif finding problem through a systematic comparison of state-of-the-art algorithms on both real and artificial networks of different sizes. We aim to provide readers a complete overview of the performance of the various tools. As far as we know, this is the most comprehensive experimental review of motif finding algorithms to date, with respect both to the number of compared tools and to the variety and size of networks used for the experiments.",
keywords = "Network motifs, Network motifs search, Network motifs significance, Network motifs tools comparison",
author = "Emanuele Martorana and Roberto Grasso and Giovanni Micale and Salvatore Alaimo and Dennis Shasha and Rosalba Giugno and Alfredo Pulvirenti",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
year = "2024",
doi = "10.1007/978-3-031-55248-9_12",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "250--267",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}