Seeding for pervasively overlapping communities

In some social and biological networks, the majority of nodes belong to multiple communities. It has recently been shown that a number of the algorithms that are designed to detect overlapping communities do not perform well in such highly overlapping settings. Here, we consider one class of these algorithms, those which optimize a local fitness measure, typically by using a greedy heuristic to expand a seed into a community. We perform synthetic benchmarks which indicate that an appropriate seeding strategy becomes increasingly important as the extent of community overlap increases. We find that distinct cliques provide the best seeds. We find further support for this seeding strategy with benchmarks on a Facebook network and the yeast interactome.Comment: 8 Page

La macération pelliculaire dans la vinification en blanc - Incidence sur la composante volatile des vins

Skin contact in white wine processing- Effects on the volatile constituents of winesWines were made from three different white vine cultivars, Chenin, Chardonnay, Bourboulenc by standard white wine processing, with and without skin contact, and their volatile constituents were analysed using gas chromatography standard procedures. 61 substances were identified and quantified in quadruplicate. One-way analyses of variance for each component were performed in which skin contact wine was compared to the control wine for each vine cultivar. Skin contact was shown to increase significantly most volatile components except volatile acids. In attempt to classify the wines investigated, principal component analysis was conducted using the amounts of compounds differing significantly in variance analysis

Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.Comment: 18 pages, 9 figure

Finding community structure in networks using the eigenvectors of matrices

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a new centrality measure that identifies those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.Comment: 22 pages, 8 figures, minor corrections in this versio

Detecting the overlapping and hierarchical community structure of complex networks

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.Comment: 20 pages, 8 figures. Final version published on New Journal of Physic

Overlapping Community Discovery Methods: A Survey

The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time. This review gives a description of the main proposals in the field. Besides the methods designed for static networks, some new approaches that deal with the detection of overlapping communities in networks that change over time, are described. Methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes. For each of them we also report, when available, computational complexity and web site address from which it is possible to download the software implementing the method.Comment: 20 pages, Book Chapter, appears as Social networks: Analysis and Case Studies, A. Gunduz-Oguducu and A. S. Etaner-Uyar eds, Lecture Notes in Social Networks, pp. 105-125, Springer,201

Multianalyte Sensing Of Addictive Over-the-counter (otc) Drugs

A supramolecular sensor array composed of two fluorescent cucurbit[n]uril-type receptors (probe 1 and probe 2) displaying complementary selectivities was tested for its ability to detect and quantify drug-related amines. The fluorimetric titration of the individual probes showed highly variable and cross-reactive analyte-dependent changes in fluorescence. An excellent ability to recognize a variety of analytes was demonstrated in qualitative as well as quantitative assays. Importantly, a successful quantitative analysis of several analytes of interest was achieved in mixtures and in human urine. The throughput and sensitivity surpass those of the current state-of-the-art methods that usually require analyte solid-phase extraction (SPE). These results open up the opportunity for new applications of cucurbit[n]uril-type receptors in sensing and pave the way for the development of simple high-throughput assays for various drugs in the near future

Impact of volatile phenols and their precursors on wine quality and control measures of Brettanomyces/Dekkera yeasts

Volatile phenols are aromatic compounds and one of the key molecules responsible for olfactory defects in wine. The yeast genus Brettanomyces is the only major microorganism that has the ability to covert hydroxycinnamic acids into important levels of these compounds, especially 4-ethylphenol and 4-ethylguaiacol, in red wine. When 4-ethylphenols reach concentrations greater than the sensory threshold, all wine’s organoleptic characteristics might be influenced or damaged. The aim of this literature review is to provide a better understanding of the physicochemical, biochemical, and metabolic factors that are related to the levels of p-coumaric acid and volatile phenols in wine. Then, this work summarizes the different methods used for controlling the presence of Brettanomyces in wine and the production of ethylphenols

The Overlapping Community Structure of Structural Brain Network in Young Healthy Individuals

Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas) in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions) can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of informa- tion through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain