Kernels on Graphs as Proximity Measures

The idea of Belknap's four-valued logic is that modern computers should function normally not only with the true values of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other). For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values (1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

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Comparison of Proximity Measures for a Topological Discrimination

Advances in Knowledge Discovery and Management - Studies in Computational Intelligence ◽

10.1007/978-3-319-45763-5_5 ◽

2016 ◽

pp. 85-99

Author(s):

Rafik Abdesselam ◽

Fatima-Zahra Aazi

Keyword(s):

Proximity Measures

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Proximity Measures

Chapman & Hall/CRC Computer Science & Data Analysis - Exploratory Data Analysis with MATLAB ◽

10.1201/9780203483374.axa ◽

2004 ◽

Keyword(s):

Proximity Measures

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Export Product Scope of Russian Firms and Proximity to Local Comparative Advantages

Economic Policy ◽

10.18288/1994-5124-2021-1-56-81 ◽

2021 ◽

Vol 16 (1) ◽

pp. 56-81

Keyword(s):

Russian Regions ◽

Economic Activities ◽

Manufacturing Enterprises ◽

Comparative Advantages ◽

Product Scope ◽

Policy Measures ◽

Proximity Measures ◽

The Given ◽

The Relationship ◽

Export Product

This paper explores the relationship between the structure of regional comparative advantages and the dynamics of the export product scope of Russian manufacturing enterprises. For this purpose, indices of revealed technological proximity of industries are calculated on the basis of data on types of economic activities and the export product scope of Russian enterprises. The methodology for calculating these indicators is based on the assumption that technologically closer types of activities are, to a certain degree, more often co-produced and co-exported within the boundaries of individual enterprises. This measure of technological proximity has several advantages over the traditionally used indicators. Estimates show that the constructed indices reflect different aspects of technological proximity of industries and can be considered as composite indicators. Technological proximity measures are used to calculate the index of product proximity to the structure of export comparative advantages of Russian regions. This index is statistically related to the probability of a product being included in the export product scope of a Russian exporter, to the probability of the product being excluded from the export product scope and, as a result, to the value and dynamics of exports of this product by the regional enterprises. These findings indicate that there is a relationship between the current structure of the regional comparative advantage and the direction in which the range of exports of Russian enterprises located in this region evolves. The results of the study can be used for designing economic policy measures aimed at diversification of production and export of the Russian regions, in particular on the basis of existing producers and exporters, as well as applied by the firms themselves to detect the most promising directions of activity expansion taking into account the production structure of the region which the given firm is located at.

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Similarity/Proximity Measures between Nodes

Algorithms and Models for Network Data and Link Analysis ◽

10.1017/cbo9781316418321.003 ◽

2016 ◽

pp. 59-101

Author(s):

Francois Fouss ◽

Marco Saerens ◽

Masashi Shimbo

Keyword(s):

Proximity Measures

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Extending Proximity Measures to Attributed Networks for Community Detection

Complex Systems ◽

10.25088/complexsystems.30.4.441 ◽

2021 ◽

Vol 30 (4) ◽

pp. 441-455

Author(s):

Rinat Aynulin ◽

◽

Pavel Chebotarev ◽

◽

Keyword(s):

Network Analysis ◽

Community Detection ◽

Network Structure ◽

Real World ◽

Adjacency Matrix ◽

Attribute Information ◽

Node Attributes ◽

Proximity Measures ◽

Definition Of ◽

Attributed Networks

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

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Sensitivity Analysis of Spatial Autocorrelation Using Distinct Geometrical Settings

International Journal of Agricultural and Environmental Information Systems ◽

10.4018/ijaeis.2016010105 ◽

2016 ◽

Vol 7 (1) ◽

pp. 65-77 ◽

Cited By ~ 3

Author(s):

António Manuel Rodrigues ◽

José António Tenedório

Keyword(s):

Sensitivity Analysis ◽

Spatial Autocorrelation ◽

Count Data ◽

Data Aggregation ◽

Scientific Field ◽

Social Phenomena ◽

Resident Population ◽

Geometrical Data ◽

Proximity Measures ◽

Global Spatial Autocorrelation

Inferences based on spatial analysis of areal data depend greatly on the method used to quantify the degree of proximity between spatial units - regions. These proximity measures are normally organized in the form of weights matrices, which are used to obtain statistics that take into account neighbourhood relations between agents. In any scientific field where the focus is on human behaviour, areal datasets are greatly relevant since this is the most common form of data collection (normally as count data). The method or schema used to divide a continuous spatial surface into sets of discrete units influences inferences about geographical and social phenomena, mainly because these units are neither homogeneous nor regular. This article tests the effect of different geometrical data aggregation schemas - administrative regions and hexagonal surface tessellation - on global spatial autocorrelation statistics. Two geographical variables are taken into account: scale (resolution) and form (regularity). This is achieved through the use of different aggregation levels and geometrical schemas. Five different datasets are used, all representing the distribution of resident population aggregated for two study areas, with the objective of consistently test the effect of different spatial aggregation schemas.

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