Large Number Of Real-world Problems Research Articles

Ranking influential nodes in complex network using edge weight degree based shell decomposition

Identifying critical nodes in a complex network or ranking the nodes based on their influence over the network has many utilities. As an example, these come in handy while choosing customers for viral marketing of a new product or identifying users whom to block for preventing the spreading of misinformation/rumor among others. With proper network modeling, a large number of real-world problems could be analyzed using such node ranking methods. Ranking the nodes based on their spreading influence in a complex network is an important research problem. Network topology-based techniques play a significant role in various applications that require knowing the central entities in a modeled system. Classical network centralities like degree, betweenness, and closeness followed by core decomposition techniques are applied successfully in ranking nodes. Each of them having certain limitations like purely local measure or computation heavy etc. Due to the inherent nature of the real-world problems/events, when they are modeled as a network, most of them transform into a weighted network. Even then a large number of literature is focused on unweighted networks due to the simplicity of computations and evaluation metrics. Many node ranking methods developed for unweighted networks may be employed to weighted networks but they will not be utilize the extra information available as connection strengths. Recently, many authors have started to extend the node ranking heuristics for the weighted networks. In this paper, we propose an edge weight degree neighborhood (EwDN) based shell decomposition technique to rank the nodes of a weighted network. It considers both node degree and edge weight with a proper balance of importance. The SIR epidemic model has been used as a benchmark for simulating the spreading process. The number of generated shells, number of core–shell nodes, and monotonicity have been computed as performance metrics during the evaluation of the methods. Also, Kendall’s rank correlation with the benchmark SIR model for various competing heuristics is compared. A performance comparison with recent relevant methods shows a better outcome with the proposed method on real-world weighted networks.

Continuity, differentiability and semismoothness of generalized tensor functions

<p style='text-indent:20px;'>A large number of real-world problems can be transformed into mathematical problems by means of third-order real tensors. Recently, as an extension of the generalized matrix function, the generalized tensor function over the third-order real tensor space was introduced with the aid of a scalar function based on the T-product for third-order tensors and the tensor singular value decomposition; and some useful algebraic properties of the function were investigated. In this paper, we show that the generalized tensor function can inherit a lot of good properties from the associated scalar function, including continuity, directional differentiability, Fréchet differentiability, Lipschitz continuity and semismoothness. These properties provide an important theoretical basis for the studies of various mathematical problems with generalized tensor functions, and particularly, for the studies of tensor optimization problems with generalized tensor functions.</p>

Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods

Abstract A large number of real-world problems require optimising several objective functions at the same time, which are generally in conflict. Many of these problems have been addressed through multi-objective evolutionary algorithms. In this paper, we propose a new hybrid evolutionary algorithm whose main feature is the incorporation of the Decision Maker’s (DM’s) preferences through multi-criteria ordinal classification methods in early stages of the optimisation process, being progressively updated. This increases the selective pressure towards the privileged zone of the Pareto front more in agreement with the DM’s preferences. An extensive experimental research was conducted to answer three main questions: i) to what extent the proposal improves the convergence towards the region of interest for the DM; ii) to what extent the proposal becomes more relevant as the number of objectives increases, and iii) to what extent the effectiveness of the hybrid algorithm depends on the particular multi-criteria method used to assign solutions to ordered classes. The issues used to evaluate our proposal and answer the questions were seven scalable test problems from the DTLZ test suite and some instances of project portfolio optimisation problems, with three and eight objectives. Compared to MOEA/D and MOEA/D-DE, the results showed that the proposed strategy obtains a better convergence towards the region of interest for the DM and also performs better characterisation of that zone on a wide range of objective functions.

NOESIS: A Framework for Complex Network Data Analysis

Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.

Learning Graph Dynamics using Deep Neural Networks

A large number of real-world problems have high dimensional data. The data obtained from these problems is highly structured and usually in the form of graphs. Graphs represent spatial information about the system in the form of vertices and edges. Often graphs evolve with time and the underlying system exhibits dynamic behavior. Hence, these graphs contain both spatial and temporal information about the system. Understanding, visualizing, and learning large graphs is of key importance for understanding the underlying system and is a challenging task due to the data deluge problem.Our work here utilizes both spatial and temporal information from structured graphs. We learn spatial and temporal information using a specific type of neural network model. Our model is robust to the kind of graphs and their dynamics of evolution. Our approach is scalable to not only the size of the graph (number of vertices and edges) but also the number of attributes (features) of the data. We show that our approach is simple, generic, parallelizable, and performs at-par with the state-of-the-art techniques. We also compare the results of our model against other existing techniques.

Construction and comparison of multi-model and mixed-model assembly lines balancing problems with bi-objective

Mass customization makes more important for assembly line balancing problems. In a large number of real-world problems, production managers face multiple, often conflicting, decision criteria. Accordingly, mixed-model and multi-model of assembly lines balancing problems (MiALBP and MuALBP) with bi-objective are considered in this paper. Firstly, two efficient mathematical programming models, MiALBP and MuALBP with bi-objective, are constructed. The first priority objective is to minimize total cycle time under a given number of workstations, and the second priority objective is related to horizontal balancing. Three kinds of objective functions are used to represent the degree of horizontal balancing. Two examples are chosen as the basis for the comparisons of these two models. The computational results of total cycle time minimization indicate that MiALBP model is superior to MuALBP one. However, MuALBP is better than MiALBP in horizontal balancing under given total cycle time.