Mining (maximal) span-cores from temporal networks

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain $T$ under analysis, the total number of span-cores is quadratic in $|T|$ as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data

Mining attribute evolution rules in dynamic attributed graphs

A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and the set of vertices in a pattern to be fixed, while others consider that a single vertex may influence its neighbors. As a result, these algorithms are unable to find complex patterns that show the influence of multiple vertices on many other vertices in terms of several attributes and different trends. This paper addresses this issue by proposing to discover a novel type of patterns called attribute evolution rules (AER). These rules indicate how changes of attribute values of multiple vertices may influence those of others with a high confidence. An efficient algorithm named AER-Miner is proposed to find these rules. Experiments on real data show AER-Miner is efficient and that AERs can provide interesting insights about dynamic attributed graphs

Spectral Measures and Generating Series for Nimrep Graphs in Subfactor Theory II: SU(3)

We complete the computation of spectral measures for SU(3) nimrep graphs arising in subfactor theory, namely the SU(3) ADE graphs associated with SU(3) modular invariants and the McKay graphs of finite subgroups of SU(3). For the SU(2) graphs the spectral measures distill onto very special subsets of the semicircle/circle, whilst for the SU(3) graphs the spectral measures distill onto very special subsets of the discoid/torus. The theory of nimreps allows us to compute these measures precisely. We have previously determined spectral measures for some nimrep graphs arising in subfactor theory, particularly those associated with all SU(2) modular invariants, all subgroups of SU(2), the torus, SU(3), and some SU(3) graphs.Comment: 38 pages, 21 figure

The streamlined genome of Phytomonas spp. relative to human pathogenic kinetoplastids reveals a parasite tailored for plants

Members of the family Trypanosomatidae infect many organisms, including animals, plants and humans. Plant-infecting trypanosomes are grouped under the single genus Phytomonas, failing to reflect the wide biological and pathological diversity of these protists. While some Phytomonas spp. multiply in the latex of plants, or in fruit or seeds without apparent pathogenicity, others colonize the phloem sap and afflict plants of substantial economic value, including the coffee tree, coconut and oil palms. Plant trypanosomes have not been studied extensively at the genome level, a major gap in understanding and controlling pathogenesis. We describe the genome sequences of two plant trypanosomatids, one pathogenic isolate from a Guianan coconut and one non-symptomatic isolate from Euphorbia collected in France. Although these parasites have extremely distinct pathogenic impacts, very few genes are unique to either, with the vast majority of genes shared by both isolates. Significantly, both Phytomonas spp. genomes consist essentially of single copy genes for the bulk of their metabolic enzymes, whereas other trypanosomatids e.g. Leishmania and Trypanosoma possess multiple paralogous genes or families. Indeed, comparison with other trypanosomatid genomes revealed a highly streamlined genome, encoding for a minimized metabolic system while conserving the major pathways, and with retention of a full complement of endomembrane organelles, but with no evidence for functional complexity. Identification of the metabolic genes of Phytomonas provides opportunities for establishing in vitro culturing of these fastidious parasites and new tools for the control of agricultural plant disease. © 2014 Porcel et al

Fermi contact interaction and spin density distribution in the Mn<SUP>2</SUP> ion: an X<SUB>&#945;</SUB> study using theoretical exchange parameters

The Fermi contact interaction and spin density distribution in the Mn+2 ion were studied by a spin-polarized X&#945; calculation, using theoretical and empirical exchange parameters &#945;. Theoretical &#945; values, &#945;t, with different &#945; for different spin, where &#945;t&#8593; &lt; &#945;t&#8595;, are necessary to predict Fermi contact terms and spin density distributions of the correct sign and magnitude; the Latter correction to the X&#945; potential did not improve the fit with the Hartree-Fock and experimental values