Dataset Information

Sequential detection of temporal communities by estrangement confinement.

ABSTRACT: Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Here, motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables one to find meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement thus provides a principled approach to uncovering temporal communities in evolving networks.

SUBMITTER: Kawadia V

PROVIDER: S-EPMC3494021 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Sequential detection of temporal communities by estrangement confinement.

Kawadia Vikas V Sreenivasan Sameet S

Scientific reports 20121109

Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitiv ...[more]

PMID: 23145317

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Project description:Marine sponges host dense, diverse, and species-specific microbial communities around the globe; however, most of the current knowledge is restricted to species from tropical and temperate waters. Only recently, some studies have assessed the microbiome of a few Antarctic sponges; however, contrary to low mid-latitude sponges, the knowledge about temporal (stability) patterns in the bacterial communities of Antarctic sponges is absent. Here, we studied the temporal patterns of bacterial communities in the Antarctic sponges Mycale (Oxymycale) acerata, Isodictya sp., Hymeniacidon torquata, and Tedania (Tedaniopsis) wellsae that were tagged in situ and monitored during three austral summers over a 24-month period. By using amplicon sequencing of the bacterial 16S rRNA gene we found that the microbiome differed between species. In general, bacterial communities were dominated by gammaproteobacterial OTUs; however, M. acerata showed the most distinct pattern, being dominated by a single betaproteobacterial OTU. The analysis at OTU level (defined at 97% sequence similarity) showed a highly stable bacterial community through time, despite the abnormal seawater temperatures (reaching 3°C) and rates of temperature increase of 0.15°C day-1 recorded in austral summer 2017. Sponges were characterized by a small core bacterial community that accounted for a high percentage of the abundance. Overall, no consistent changes in core OTU abundance were recorded for all studied species, confirming a high temporal stability of the microbiome. In addition, predicted functional pathway profiles showed that the most abundant pathways among all sponges belonged mostly to metabolism pathway groups (e.g., amino acid, carbohydrate, energy, and nucleotide). The predicted functional pathway patterns differed among the four sponge species. However, no clear temporal differences were detected supporting what was found in terms of the relatively stable composition of the bacterial communities.

Dataset Information

Sequential detection of temporal communities by estrangement confinement.

Publications

Sequential detection of temporal communities by estrangement confinement.

Similar Datasets

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