Large-Scale Structure of a Network of Co-Occurring MeSH Terms: Statistical Analysis of Macroscopic Properties

• Published in: PloS one Vol. 9; no. 7; p. e102188
• Main Authors: Kastrin, Andrej, Rindflesch, Thomas C., Hristovski, Dimitar
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.07.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bioinformatics; Biology and Life Sciences; Clustering; Coefficients; Computational Biology - methods; Computer and Information Sciences; Computer simulation; Controlled vocabularies; Finite element method; Humans; Knowledge; Kolmogorov-Smirnov test; Language; Medical research; Medical Subject Headings; Models, Statistical; Modular construction; Modularity; Natural language processing; Network reduction; Networks; Nodes; Physical Sciences; Power law; Principal Component Analysis; Properties (attributes); Social networks; Social Sciences; Statistical analysis; Statistical tests; United States > US; Slovenia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0102188

Concept associations can be represented by a network that consists of a set of nodes representing concepts and a set of edges representing their relationships. Complex networks exhibit some common topological features including small diameter, high degree of clustering, power-law degree distribution, and modularity. We investigated the topological properties of a network constructed from co-occurrences between MeSH descriptors in the MEDLINE database. We conducted the analysis on two networks, one constructed from all MeSH descriptors and another using only major descriptors. Network reduction was performed using the Pearson's chi-square test for independence. To characterize topological properties of the network we adopted some specific measures, including diameter, average path length, clustering coefficient, and degree distribution. For the full MeSH network the average path length was 1.95 with a diameter of three edges and clustering coefficient of 0.26. The Kolmogorov-Smirnov test rejects the power law as a plausible model for degree distribution. For the major MeSH network the average path length was 2.63 edges with a diameter of seven edges and clustering coefficient of 0.15. The Kolmogorov-Smirnov test failed to reject the power law as a plausible model. The power-law exponent was 5.07. In both networks it was evident that nodes with a lower degree exhibit higher clustering than those with a higher degree. After simulated attack, where we removed 10% of nodes with the highest degrees, the giant component of each of the two networks contains about 90% of all nodes. Because of small average path length and high degree of clustering the MeSH network is small-world. A power-law distribution is not a plausible model for the degree distribution. The network is highly modular, highly resistant to targeted and random attack and with minimal dissortativity.