Multicriteria rankings of open-end investment funds and their stability

For research purposes, three multicriteria outranking methods (PROMETHEE, WSA and TOPSIS) were used to construct rankings of investment funds to assess their performance in the time period from January to July 2008. Nine indicators related to the distributions of return rates, purchase and management costs and to customers’ convenience were included in the set of criteria. The weight of each criterion was calculated on the basis of the relative volatility rate of the given criterion. In order to assess the stability of the rankings, the weight of a single criterion was changed (using each criterion in turn) and new rankings were constructed using the modified weights. The similarity of rankings built before and after these changes was assessed on the basis of the maximum difference between ranks and the Spearman correlation coefficient. The results obtained enable assessment not only of the stability of each outranking method, but the similarity of results obtained by different methods as well. All calculations were done using the SANNA software.investment funds, outranking methods, PROMETHEE method, WSA method, TOPSIS method, stability of rankings

Network Model Selection Using Task-Focused Minimum Description Length

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology

Quantum Google in a Complex Network

We investigate the behavior of the recently proposed quantum Google algorithm, or quantum PageRank, in large complex networks. Applying the quantum algorithm to a part of the real World Wide Web, we find that the algorithm is able to univocally reveal the underlying scale-free topology of the network and to clearly identify and order the most relevant nodes (hubs) of the graph according to their importance in the network structure. Moreover, our results show that the quantum PageRank algorithm generically leads to changes in the hierarchy of nodes. In addition, as compared to its classical counterpart, the quantum algorithm is capable to clearly highlight the structure of secondary hubs of the network, and to partially resolve the degeneracy in importance of the low lying part of the list of rankings, which represents a typical shortcoming of the classical PageRank algorithm. Complementary to this study, our analysis shows that the algorithm is able to clearly distinguish scale-free networks from other widespread and important classes of complex networks, such as Erd\H{o}s-R\'enyi networks and hierarchical graphs. We show that the ranking capabilities of the quantum PageRank algorithm are related to an increased stability with respect to a variation of the damping parameter $\alpha$ that appears in the Google algorithm, and to a more clearly pronounced power-law behavior in the distribution of importance among the nodes, as compared to the classical algorithm. Finally, we study to which extent the increased sensitivity of the quantum algorithm persists under coordinated attacks of the most important nodes in scale-free and Erd\H{o}s-R\'enyi random graphs

A comparative study on Spanish regions’ investment capacity in a budgetary discipline anticipated scenario, by means of multicriteria Promethee method.

The principle of the budgetary discipline, compulsory for the Spanish regions by the Law 18/2001, December 12th [General Law of Budgetary Stability] and the Organic Law 5/2001, December 13th, complementary to the former one, established in the frame of the European Agreement for Stability and Growth, can generate conflicting situations with those Spanish regions which investment capacity depends on external borrowing. This paper deals with the corresponding relative position of the different regions, according to its investment capacity, using for that purpose a simulation exercise, in which we advance the budgetary stability constraint for the period 1997-2000. In this paper, the public financial activity is treated, for each region, through different public revenue and expenditure ratios per capita. This situation leads to consider a multicriteria Promethee method as the apropriate one to obtain a global ranking for all of them. In the opinion of Al-Shemmeri, Al-Kloub and Rearman (1997), this method is the most adequate one because of the following advantages: public authorities, as decision takers, can understand easily the results, regardless the knowledge they may have about it; the method uses understandable economic parameters; the method avoids distorting scale effects among different alternatives and, as well, makes possible the deviation evaluation between alternatives and, finally, allows for sensibility analysis.

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

Reference gene selection and RNA preservation protocol in the cat flea, Ctenocephalides felis, for gene expression studies

This work was supported by a Knowledge Transfer Network BBSRC Industrial Case (#414 BB/L502467/1) studentship in association Zoetis Inc.Peer reviewedPostprin