Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio

Identifying networks with common organizational principles

Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this paper, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.Comment: 26 pages, 7 figure

On creep-fatigue endurance of TIG-dressed weldments using the linear matching method

This paper is devoted to parametric study on creep-fatigue endurance of the steel type 316N(L) weldments at 550◦C identified as type 3 according to R5 Vol. 2/3 procedure classification. The study is implemented using a direct method known as the Linear Matching Method (LMM) and based upon the creep-fatigue evaluation procedure considering time fraction rule for creep-damage assessment. Seven configurations of the weldment, characterised by particular values of a geometrical parameter ρ, are proposed. Parameter ρ, which represents different grades of TIG dressing, is a ratio between the radius of the fillet of the remelted metal on a weld toe and the thickness of welded plates. For each configuration, the total number of cycles to failure N⋆ in creep-fatigue conditions is assessed numerically for different loading cases defined by normalised bending moment ˜M and dwell period t. The obtained set of N⋆ is extrapolated by the analytic function dependent on ˜M, t and parameter ρ. Proposed function for N⋆ shows good agreement with numerical results obtained by the LMM. Therefore, it is used for the identification of Fatigue Strength Reduction Factors (FSRFs) effected by creep, which are intended for design purposes, and dependent on t and ρ

A parametric study on creep-fatigue strength of welded joints using the linear matching method

This paper presents a parametric study on creep-fatigue strength of the steel AISI type 316N(L) weldments of types 1 and 2 according to R5 Vol. 2/3 Procedure classification at 550◦C. The study is implemented using the Linear Matching Method (LMM) and is based upon a latest developed creep-fatigue evaluation procedure considering time fraction rule for creep-damage assessment. Parametric models of geometry and FE-meshes for both types of weldments are developed in this way, which allows variation of parameters governing shape of the weld profile and loading conditions. Five configurations, characterised by individual sets of parameters, and presenting different fabrication cases, are proposed. For each configuration, the total number of cycles to failure N⋆ in creep-fatigue conditions is assessed numerically for different loading cases including normalised bending moment M and dwell period t. The obtained set of N⋆ is extrapolated by the analytic function, which is dependent on M, t and geometrical parameters (α and β). Proposed function for N⋆ shows good agreement with numerical results obtained by the LMM. Thus, it is used for the identification of Fatigue Strength Reduction Factors (FSRFs) intended for design purposes and dependent on t, α, β