In a very general context, communities in networks are defined as groups of nodes that have some common properties such that connections are stronger between the nodes in a community than with the nodes in the rest of the network. It is quite common for nodes to participate in multiple communities. Therefore a community detection algorithm for such applications should be able to detect overlapp...

Our aim was to evaluate the diagnostic accuracy and clinical utility of a serotype-specific urinary antigen detection multiplex assay for identification of 13 pneumococcal serotypes (1, 3, 4, 5, 6A, 6B, 7F, 9V, 14, 18C, 19A, 19F and 23F) in urine of patients with community-acquired pneumonia. Adult patients with clinical suspicion of community-acquired pneumonia were included. In addition to st...

introduction: methicillin-resistant staphylococcus aureus (mrsa) is responsible for an increasing number of serious hospital- and community-acquired infections. usa300 is known to be the most common cause of community-acquired infections, but recently there have been some reports on hospital-acquired infections caused by this strain. methods: totally 171 isolates of s. aureus were collected fro...

We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidd...

Effectively and accurately assessing total microbial community diversity is one of the primary challenges in modern microbial ecology. This is particularly true with regard to the detection and characterization of unculturable populations and those present only in low abundance. We report a novel strategy, GC fractionation combined with denaturing gradient gel electrophoresis (GC-DGGE), which c...

The detection of community structure is a widely recognised method of revealing the underlying properties of complex networks in biological, physical and social sciences. The simplest form of the community structure problem is the partitioning of unweighted and undirected networks into disjoint communities. However, module detection in weighted networks or communities with overlapping modules m...

Subgroup discovery and community detection are two approaches having been studied in different research areas like data mining and social network analysis. In this context, these techniques are especially helpful in order to provide for analytical and explorative data mining approaches. We present an organized picture of recent research in subgroup discovery and community detection specifically...

Community detection is an important part of network analysis. The goal is simple: to detect how nodes in the graph ought to be grouped into communities. Some algorithms wholly partition the nodes; others allow for some nodes to be considered ”community-less”. Community detection has several applications: for example, suggesting connections (as in, say, Facebook) and determining network structur...

Community structure is an interesting feature found in many social networks which signifies that there is intense interaction between some individuals. These communities have a tendency to overlap with each other as there are nodes that can belong to multiple communities simultaneously. Detection of such overlapping communities is a challenging task; it still remains a topic of interest for the...