Entified for the comprehensive 20year window collapsed into a single network.
Entified for the complete 20year window collapsed into a single network. Fig. visualizes the community identifications for the full network (Panel A), and separately for AIDS and JAIDS (Panels B and C, respectively). The network PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/24367588 is clustered into distinct communities (modularity50.469), and is dominated by 3 main communities (colored red, blue and yellow respectively), with quite a few smaller sized communities which are peripheral to a single of these 3 (six, colored orange, is peripherally connected to 3) or two of these bigger communities (4, magenta, and five, green, are peripheral to and 2, respectively). As of 999, both journals introduced write-up classifications of “Basic,” “Clinical” or “Social and Epidemiological” Sciences, which had been applied for the vast majority of subsequently, published articles. The correspondence in between the 3 biggest bibliographic coupling network communities and these broad “discipline” like labels is pronounced (presented in Panel D) with every neighborhood dominated by one such label (as marked by its overrepresentation and also the substantial underrepresentation of each in the other individuals ,Clinical, 2,Basic, three,Social Epidemiological). The identified disciplinebased arrangement of communities is just not dependent on which community resolution is applied. A 3community option was also identified which only exacerbates this pattern. Similarly, options with larger numbers of 
communities had been nested within those presented, i.e producing finer divisions inside, not SHP099 (hydrochloride) web bridging across the disciplinebased communities. The emergent communities based on citation overlaps offer initial indication of your persistence of disciplinary boundaries based on the broad categorizationsbasic, clinical, and socialepidemiological scientificwithin this crosssectional view. A dynamic approach that considers topic consolidation complicates this initial overview. Next we ask how these observed communities account for major drivers in the modularity involving HIVAIDS research regions. The report labels talked about above hint at a few of these bases (i.e somewhat determined by a “disciplinary” orientation), but to formalize this further, we examine how readily the bibliographic coupling community structure corresponds with all the 30 identified subjects that summarize the content of HIVAIDS analysis (see S2 Figure for more information on subject labeling). Seventeen subjects have been comparatively “consolidated” (i.e extremely represented in only neighborhood), that is constant with an interdisciplinary approach (e.g drug metabolism is consolidated in Cluster the red cluster in Fig. that is definitely much more related with clinical study, when vaccine development is consolidated in Cluster 2bluebasic science; for any full list with the consolidated subjects, see S3 Figure). Fig. two presents a mosaic plot representing correspondence for those 3 subjects which might be spread more than extra than community (see S3 Figure for the correspondence of all 30 topics). One example is, “ARV3” is usually a subject about toxicities in clinical trials for antiretrovirals (ARV), which can be significantly represented inPLOS One DOI:0.37journal.pone.05092 December five,six Bibliographic Coupling in HIVAIDS ResearchFig. two. CommunityTopic (lack of) Correspondence. This mosaic plot shows these topics which can be overrepresented present in much more than 1 network neighborhood (major ), or usually are not consolidated in any community (bottom two). The subjects are derived through LDA (see Supplementary Details) plus the communities are those rep.