E the evolution of patterns more than two decades. Initially, for everyE the evolution of

E the evolution of patterns more than two decades. Initially, for every
E the evolution of patterns over two decades. First, for every single pair of papers in the corpus, we construct a papertopaper bibliographic coupling network [2, 22]. To construct the bibliographic coupling network, we use data preprocessing capabilities in [23] to compute the extent to which papers in our corpus (N56,907) jointly cite precisely the same papers, using cosineweighted citedreference Olmutinib web similarity scores [24]; final results didn’t differ appreciably when alternatively employing weights primarily based on very simple citation counts or Jaccard similarity [25]. All bibliographiccoupling network analyses presented inside the paper rely on these totally weighted cited reference similarity scores. Even so, to cut down some of the noise in visualizations, the network representations in Fig. recode this similarity matrix to dichotomous presence absence of ties involving paper pairs with similarity scores that exceed the mean score plus two common deviations; this computation excludes all isolates (i.e those papers that share no citations with any other papers within the corpus). Second, we analyze these networks with neighborhood detection approaches, which determine segmentation within a network [26, 27]. Formally, this really is typically computed as locating blocks in the network for which some majority of ties are formed within the group and relatively few ties are formed outside these groups [27]. There are various methods for locating network communities; here we use the fastgreedy algorithm [28] for computing the Newman and Girvan [26] index as implemented in igraph 0.six [29] for R 3.0.; benefits didn’t differ appreciably when utilizing the Louvain technique as an option [30]. Modularity maximization is really a typical method for acquiring the number of communities inside a graph and canPLOS One DOI:0.37journal.pone.05092 December five,3 Bibliographic Coupling in HIVAIDS ResearchFig. . Bibliographic Coupling Network Communities inside the Comprehensive Corpus. Panel A presents the full bibliographic coupling network, edgereduction is based on papers with weighted similarity scores two regular deviations above the median similarity among nonisolates in the network. Node colour represents each paper’s identified bibliographic coupling community utilizing the NewmanGirvan algorithm [26]. Panels B and C present precisely the same analyses restricted only to publications from AIDS and JAIDS respectively. Panel D show the correspondence between communities as well as the broad “discipline” like labels applied to all published articles starting in 998. Colour represents no matter if a label is more than (blue) or under (red) represented inside a provided neighborhood according permutationbased residuals. doi:0.37journal.pone.05092.gbe made use of to describe how readily the identified communities account for the structure of an observed network [3]. Modularity scores represent locally maximized functions that determine how readily ties form inside as opposed to across communities. Our benefits beneath depend on options that identify involving six communities identified (based around the period). When the raw interpretation of modularity scores is uncommon, comparison across networks with similar numbers of nodes and ties can reveal any substantial modifications in community structure over time [27], which we summarize by plotting the structural modifications over time. We then use an Alluvial Flow diagram described in [32] to visualize how the detected communities adjust more than time.PLOS A single PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/24126911 DOI:0.37journal.pone.05092 December five,4 Bibliographic Coupling in HIVAIDS ResearchThird, sinc.