2013/01/01 by Corrie Jacobien Carstens, K. J. Horadam · 1 citation
Computer Science · Physics and Astronomy · Biochemistry, Genetics and Molecular Biology · Mathematics · #Topological and Geometric Data Analysis #Complex Network Analysis Techniques #Bioinformatics and Genomic Networks #Betti number #Persistent homology #Network motif #Binary number #Computer science #Homology (biology) #Theoretical computer science #Network topology #Topology (electrical circuits) #Network structure #Focus (optics) #Network science #Complex network #Mathematics #Discrete mathematics #Combinatorics #Algorithm #Computer network #Biology #World Wide Web #Physics #Genetics
paper · pdf · doi:10.1155/2013/815035
openalex publication_date 2013/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Over the past few decades, network science has introduced several statistical measures to determine the topological structure of large networks. Initially, the focus was on binary networks, where edges are either present or not. Thus, many of the earlier measures can only be applied to binary networks and not to weighted networks. More recently, it has been shown that weighted networks have a rich structure, and several generalized measures have been introduced. We use persistent homology, a recent technique from computational topology, to analyse four weighted collaboration networks. We include the first and second Betti numbers for the first time for this type of analysis. We show that persistent homology corresponds to tangible features of the networks. Furthermore, we use it to distinguish the collaboration networks from similar random networks.