Coloring random graphs and maximizing local diversity

We study a variation of the graph coloring problem on random graphs of finite average connectivity. Given the number of colors, we aim to maximize the number of different colors at neighboring vertices (i.e. one edge distance) of any vertex. Two efficient algorithms, belief propagation and Walksat are adapted to carry out this task. We present experimental results based on two types of random graphs for different system sizes and identify the critical value of the connectivity for the algorithms to find a perfect solution. The problem and the suggested algorithms have practical relevance since various applications, such as distributed storage, can be mapped onto this problem.

Resource allocation in sparse graphs

Resource allocation in sparsely connected networks, a representative problem of systems with real variables, is studied using the replica and Bethe approximation methods. An efficient distributed algorithm is devised on the basis of insights gained from the analysis and is examined using numerical simulations,showing excellent performance and full agreement with the theoretical results. The physical properties of the resource allocation model are discussed.

Message passing for task redistribution on sparse graphs

The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

An ideal ocular nutritional supplement?

The role of nutritional supplementation in prevention of onset or progression of ocular disease is of interest to health care professionals and patients. The aim of this review is to identify those antioxidants most appropriate for inclusion in an ideal ocular nutritional supplement, suitable for those with a family history of glaucoma, cataract, or age-related macular disease, or lifestyle factors predisposing onset of these conditions, such as smoking, poor nutritional status, or high levels of sunlight exposure. It would also be suitable for those with early stages of age-related ocular disease. Literature searches were carried out on Web of Science and PubMed for articles relating to the use of nutrients in ocular disease. Those highlighted for possible inclusion were vitamins A, B, C and E, carotenoids beta-carotene, lutein, and zeaxanthin, minerals selenium and zinc, and the herb, Ginkgo biloba. Conflicting evidence is presented for vitamins A and E in prevention of ocular disease; these vitamins have roles in the production of rhodopsin and prevention of lipid peroxidation respectively. B vitamins have been linked with a reduced risk of cataract and studies have provided evidence supporting a protective role of vitamin C in cataract prevention. Beta-carotene is active in the prevention of free radical formation, but has been linked with an increased risk of lung cancer in smokers. Improvements in visual function in patients with age-related macular disease have been noted with lutein and zeaxanthin supplementation. Selenium has been linked with a reduced risk of cataract and activates the antioxidant enzyme glutathione peroxidase, protecting cell membranes from oxidative damage while zinc, although an essential component of antioxidant enzymes, has been highlighted for risk of adverse effects. As well as reducing platelet aggregation and increasing vasodilation, Gingko biloba has been linked with improvements in pre-existing field damage in some patients with normal tension glaucoma. We advocate that vitamins C and E, and lutein/zeaxanthin should be included in our theoretically ideal ocular nutritional supplement.

Solvable model for distribution networks on random graphs

We propose a simple model that captures the salient properties of distribution networks, and study the possible occurrence of blackouts, i.e., sudden failings of large portions of such networks. The model is defined on a random graph of finite connectivity. The nodes of the graph represent hubs of the network, while the edges of the graph represent the links of the distribution network. Both, the nodes and the edges carry dynamical two state variables representing the functioning or dysfunctional state of the node or link in question. We describe a dynamical process in which the breakdown of a link or node is triggered when the level of maintenance it receives falls below a given threshold. This form of dynamics can lead to situations of catastrophic breakdown, if levels of maintenance are themselves dependent on the functioning of the net, once maintenance levels locally fall below a critical threshold due to fluctuations. We formulate conditions under which such systems can be analyzed in terms of thermodynamic equilibrium techniques, and under these conditions derive a phase diagram characterizing the collective behavior of the system, given its model parameters. The phase diagram is confirmed qualitatively and quantitatively by simulations on explicit realizations of the graph, thus confirming the validity of our approach. © 2007 The American Physical Society.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

From ideal to real:a longitudinal study of the role of implicit leadership theories on leader-member exchanges and employee outcomes

The results of the present longitudinal study demonstrate the importance of implicit leadership theories (ILTs) for the quality of leader-member exchanges (LMX) and employees' organizational commitment, job satisfaction, and well-being. Results based on a sample of 439 employees who completed the study questionnaires at 2 time points showed that the closer employees perceived their actual manager's profile to be to the ILTs they endorsed, the better the quality of LMX. Results also indicated that the implicit-explicit leadership traits difference had indirect effects on employee attitudes and well-being. These findings were consistent across employee groups that differed in terms of job demand and the duration of manager-employee relation, but not in terms of motivation. Furthermore, crossed-lagged modeling analyses of the longitudinal data explored the possibility of reciprocal effects between implicit-explicit leadership traits difference and LMX and provided support for the initially hypothesized direction of causal effects.

Competition for shortest paths on sparse graphs

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised. © 2012 American Physical Society.

Type-2 TOPSIS:a group decision problem when ideal values are not extreme endpoints

In the traditional TOPSIS, the ideal solutions are assumed to be located at the endpoints of the data interval. However, not all performance attributes possess ideal values at the endpoints. We termed performance attributes that have ideal values at extreme points as Type-1 attributes. Type-2 attributes however possess ideal values somewhere within the data interval instead of being at the extreme end points. This provides a preference ranking problem when all attributes are computed and assumed to be of the Type-1 nature. To overcome this issue, we propose a new Fuzzy DEA method for computing the ideal values and distance function of Type-2 attributes in a TOPSIS methodology. Our method allows Type-1 and Type-2 attributes to be included in an evaluation system without compromising the ranking quality. The efficacy of the proposed model is illustrated with a vendor evaluation case for a high-tech investment decision making exercise. A comparison analysis with the traditional TOPSIS is also presented. © 2012 Springer Science+Business Media B.V.

Research allocation on sparse graphs

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Algorithms for solving large random graphs

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Belief propagation on dense graphs with few strong links

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Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

The topics they are a-changing - characterising topics with time-stamped semantic graphs

DBpedia has become one of the major sources of structured knowledge extracted from Wikipedia. Such structures gradually re-shape the representation of Topics as new events relevant to such topics emerge. Such changes make evident the continuous evolution of topic representations and introduce new challenges to supervised topic classification tasks, since labelled data can rapidly become outdated. Here we analyse topic changes in DBpedia and propose the use of semantic features as a more stable representation of a topic. Our experiments show promising results in understanding how the relevance of features to a topic changes over time.

Shortest node-disjoint paths on random graphs

A localized method to distribute paths on random graphs is devised, aimed at finding the shortest paths between given source/destination pairs while avoiding path overlaps at nodes. We propose a method based on message-passing techniques to process global information and distribute paths optimally. Statistical properties such as scaling with system size and number of paths, average path-length and the transition to the frustrated regime are analyzed. The performance of the suggested algorithm is evaluated through a comparison against a greedy algorithm. © 2014 IOP Publishing Ltd and SISSA Medialab srl.