Community structure in directed networks

We consider the problem of finding communities or modules in directed networks. The most common approach to this problem in the previous literature has been simply to ignore edge direction and apply methods developed for community discovery in undirected networks, but this approach discards potentially useful information contained in the edge directions. Here we show how the widely used benefit function known as modularity can be generalized in a principled fashion to incorporate the information contained in edge directions. This in turn allows us to find communities by maximizing the modularity over possible divisions of a network, which we do using an algorithm based on the eigenvectors of the corresponding modularity matrix. This method is shown to give demonstrably better results than previous methods on a variety of test networks, both real and computer-generated.Comment: 5 pages, 3 figure

Mixture models and exploratory analysis in networks

Networks are widely used in the biological, physical, and social sciences as a concise mathematical representation of the topology of systems of interacting components. Understanding the structure of these networks is one of the outstanding challenges in the study of complex systems. Here we describe a general technique for detecting structural features in large-scale network data which works by dividing the nodes of a network into classes such that the members of each class have similar patterns of connection to other nodes. Using the machinery of probabilistic mixture models and the expectation-maximization algorithm, we show that it is possible to detect, without prior knowledge of what we are looking for, a very broad range of types of structure in networks. We give a number of examples demonstrating how the method can be used to shed light on the properties of real-world networks, including social and information networks.Comment: 8 pages, 4 figures, two new examples in this version plus minor correction

Prostaglandin E Positively Modulates Endothelial Progenitor Cell Homeostasis: An Advanced Treatment Modality for Autologous Cell Therapy

Aims: The mobilization of endothelial progenitor cells (EPC) and their functioning in postnatal neovascularization are tightly regulated. To identify new modulators of EPC homeostasis, we screened biologically active prostaglandin E compounds for their effects on EPC production, trafficking and function. Methods and Results: We found that EPC are a rich source for prostaglandin E 2 (PGE 2), stimulating their number and function in an auto- and paracrine manner. In vivo blockade of PGE 2 production by selective cyclooxygenase-2 inhibition virtually abrogated ischemia-induced EPC mobilization demonstrating its crucial role in EPC homeostasis following tissue ischemia. Conversely, ex vivo treatment of isolated EPC with the clinically approved PGE 1 analogue alprostadil enhanced EPC number and function. These effects were mediated by increased expression of the chemokine receptor CXCR4 and were dependent on nitric oxide synthase activity. Most importantly, ex vivo PGE 1 pretreatment of isolated EPC significantly enhanced their neovascularization capacity in a murine model of hind limb ischemia as assessed by laser Doppler analysis, exercise stress test and immunohistochemistry. Conclusions: The conserved role for PGE in the regulation of EPC homeostasis suggests that ex vivo modulation of the prostaglandin pathway in isolated progenitor cells may represent a novel and safe strategy to facilitate cell-based therapies. Copyright (C) 2009 S. Karger AG, Base

Elastic precession of electronic spin states in interacting integer quantum Hall edge channels

We consider the effect of Coulomb interactions in the propagation of electrons, prepared in arbitrary spin states, on chiral edge channels in the integer quantum Hall regime. Electrons are injected and detected at the same energy at different locations of the Hall bar, which is modeled as a chiral Tomonaga-Luttinger liquid. The current is computed perturbatively in the tunneling amplitudes, within a non-crossing approximation using exact solutions of the interacting Green's functions. In the case of different channel velocities, the spin precession effect is evaluated, and the role of interaction parameters and wavevectors is discussed.Comment: 5 pages, 3 figure

Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows one to study community structure in a very general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.Comment: 31 pages, 3 figures, 1 table. Includes main text and supporting material. This is the accepted version of the manuscript (the definitive version appeared in Science), with typographical corrections included her

An Artificially Lattice Mismatched Graphene/Metal Interface: Graphene/Ni/Ir(111)

We report the structural and electronic properties of an artificial graphene/Ni(111) system obtained by the intercalation of a monoatomic layer of Ni in graphene/Ir(111). Upon intercalation, Ni grows epitaxially on Ir(111), resulting in a lattice mismatched graphene/Ni system. By performing Scanning Tunneling Microscopy (STM) measurements and Density Functional Theory (DFT) calculations, we show that the intercalated Ni layer leads to a pronounced buckling of the graphene film. At the same time an enhanced interaction is measured by Angle-Resolved Photo-Emission Spectroscopy (ARPES), showing a clear transition from a nearly-undisturbed to a strongly-hybridized graphene $\pi$-band. A comparison of the intercalation-like graphene system with flat graphene on bulk Ni(111), and mildly corrugated graphene on Ir(111), allows to disentangle the two key properties which lead to the observed increased interaction, namely lattice matching and electronic interaction. Although the latter determines the strength of the hybridization, we find an important influence of the local carbon configuration resulting from the lattice mismatch.Comment: 9 pages, 3 figures, Accepted for publication in Phys. Rev.

Constraint Replacement-Based Design for Additive Manufacturing of Satellite Components: Ensuring Design Manufacturability through Tailored Test Artefacts

Additive manufacturing (AM) is becoming increasingly attractive for aerospace companies due to the fact of its increased ability to allow design freedom and reduce weight. Despite these benefits, AM comes with manufacturing constraints that limit design freedom and reduce the possibility of achieving advanced geometries that can be produced in a cost-efficient manner. To exploit the design freedom offered by AM while ensuring product manufacturability, a model-based design for an additive manufacturing (DfAM) method is presented. The method is based on the premise that lessons learned from testing and prototyping activities can be systematically captured and organized to support early design activities. To enable this outcome, the DfAM method extends a representation often used in early design, a function-means model, with the introduction of a new model construct-manufacturing constraints (Cm). The method was applied to the redesign, manufacturing, and testing of a flow connector for satellite applications. The results of this application-as well as the reflections of industrial practitioners-point to the benefits of the DfAM method in establishing a systematic, cost-efficient way of challenging the general AM design guidelines found in the literature and a means to redefine and update manufacturing constraints for specific design problems

Vertex similarity in networks

We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks