Graph Theory and Economic Models: from Small to Large Size Applications

Keller, André A.

doi:10.1016/j.endm.2007.01.065

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…An empirical study by A. Keller encompassed another application of graph theory to economicsauthor proposed to use graphs as representations of economic models, i.e. vertices are considered to be model's variables while the edges represent dependence between variables in model equations (Keller, 2007).…”

Section: H Safari Et Al Introduced a Combined Methodology Consistinmentioning

confidence: 99%

A Novel Graph Decomposition Approach to the Automatic Processing of Poorly Formalized Data: Innovative Ideas – A Management Case Study

Patrikeeva¹,

Бабешко²,

Kamenev³

et al. 2015

ERSJ

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In the following paper we present a novel approach to unstructured data processing by imposing a hierarchical graph-based structure on the data and decomposing it into separate subgraphs according to optimization criteria. In the scope of the paper we also consider the problem of automatic classification of textual data for the synthesizing the hierarchical data structure. The proposed approach uses textual information on the first stage to classify ideas, innovations, and objects of intellectual property (OIPs) to construct a multilayered graph. Numerical criteria are used to decompose constructed graph into separate subgraphs. In the scope of the research we apply the developed approach to the innovative ideas in a management case study. The research has been conducted in the scope of a joint research project with financial aid of Ministry of Education and Science of Russian Federation RFMEFI57314X0007.

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Section: H Safari Et Al Introduced a Combined Methodology Consistinmentioning

confidence: 99%

A Novel Graph Decomposition Approach to the Automatic Processing of Poorly Formalized Data: Innovative Ideas – A Management Case Study

Patrikeeva¹,

Бабешко²,

Kamenev³

et al. 2015

ERSJ

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“…The measures of stability provided in [24] can be equivalently obtained from a formal general definition of stability for sampling sets in arbitrary spaces [39]. In particular, recalling the definition of stability presented in [39] for general sampling schemes, we can say that P W ω (G) posses a stable sampling expansion or reconstruction on S ⊂ V (G) if there exists C ≥ 1 such that x(S c ) 2 2 ≤ (C − 1) x(S) 2 2 ∀x ∈ P W ω (G). The value of C provides a measure of stability associated to S; the larger the value of C the less stability we have.…”

Section: Stability and Blue-noise Sampling Setsmentioning

confidence: 99%

“…Interesting phenomena in nature can often be captured by graphs since objects and data are invariably inter-related in some sense. Social [1], financial [2], ecological networks, and the human brain [3] are a few examples of such networks. Data in these networks reside on irregular or otherwise unordered structures [4].…”

Section: Introductionmentioning

confidence: 99%

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Blue-Noise Sampling on Graphs

Parada-Mayorga

Lau

Giraldo

et al. 2019

IEEE Trans. on Signal and Inf. Process. over Networks

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In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain of the graph, as a way to conveniently find statistically good sampling sets. This is done establishing that there is a family of good sampling sets characterized on the vertex domain by a maximization of the distance between sampling nodes; in the Fourier domain, these are characterized by spectrums that are dominated by high frequencies referred to as blue-noise. The theoretical connection between blue-noise sampling on graphs and previous results in graph signal processing is also established, explaining the advantages of the proposed approach. Restricting our analysis to undirected and connected graphs, numerical tests are performed in order to compare the effectiveness of blue-noise sampling against other approaches.

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“…The graph on Fig. 12 is not a simple directed graph but a special case of graph called"network" (Keller, 2007). The network type graphs are characterized with two special nodes: one from which the edges only come out (called BEGINNING nodes) and another in which the edges only get in (called END nodes).…”

Section: Valuing the Connectivitymentioning

confidence: 99%

Quality Assessment Criteria and Their Role in the Development of a Successful Educational Project Proposal

Staneva¹,

Alexandrova

Petkov

2015

Period. Polytech. Soc. Man. Sci.

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Keywords project management, monitoring, evaluation, complex networks IntroductionThe utilization of European funds and the participation in international projects is a trivial topic that always comes on the agenda when we talk about European financing. The present study is based on the analysis of project proposals that are submitted for funding by one of the EU educational programs. This analysis broadens our view not only on the utilization of EU structural funds, but also on our participation in large international projects where educational organizations directly apply for the funding of their ideas.The present study aims to reconstruct the relationship between the evaluation criteria of the already assessed projects and thus to show the real objectives that are achieved through the funding of European educational programs, objectives that might differ from those written down in the European papers. The aim of this study is sufficiently important by its own because on the one hand, the research methodology can be used in other areas of EU financing, and on the other, it provides a method of feedback and adjustment of the assessment. In reality, by post-monitoring of the evaluation results within a certain period one can receive a feedback and can make appropriate conclusions and corrections to the methods and actions for monitoring and evaluation. The study achieves other purposes as well -for example, another important outcome from the present research is the knowledge of the mechanisms of connectivity between the different quality assessment criteria, which enables us to create an architectural model of a project proposal that may enhance its competitiveness and efficiency.

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Graph Theory and Economic Models: from Small to Large Size Applications

Cited by 10 publications

References 3 publications

A Novel Graph Decomposition Approach to the Automatic Processing of Poorly Formalized Data: Innovative Ideas – A Management Case Study

A Novel Graph Decomposition Approach to the Automatic Processing of Poorly Formalized Data: Innovative Ideas – A Management Case Study

Blue-Noise Sampling on Graphs

Quality Assessment Criteria and Their Role in the Development of a Successful Educational Project Proposal

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