Estimating the Node-Level Behaviors in Complex Networks From Structural Datasets

2013 ◽  
Author(s):  
Zhenghui Sha ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Communication Networks ◽  
Choice Model ◽  
Preferential Attachment ◽  
Network Evolution ◽  
Statistical Regression ◽  
Node Level ◽  
Endogenous Networks ◽  
Attachment Model ◽  
And Performance

There is an emerging class of networks that evolve endogenously based on the local characteristics and behaviors of nodes. Examples of such networks include social, economic, and peer-to-peer communication networks. The node-level behaviors determine the overall structure and performance of these networks. This is in contrast to exogenously designed networks whose structures are directly determined by network designers. To influence the performance of endogenous networks, it is crucial to understand a) what kinds of local behaviors result in the observed network structures and b) how these local behaviors influence the overall performance. The focus in this paper is on the first aspect, where information about the structure of networks is available at different points in time and the goal is to estimate the behavior of nodes that resulted in the observed structures. We use three different approaches to estimate the node-level behaviors. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the node-level behaviors from consecutive snapshots of the network structure. In the third approach, the nodes are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-making framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The autonomous system (AS) level Internet is used as an illustrative example to illustrate and compare the three approaches.

Estimating Local Decision-Making Behavior in Complex Evolutionary Systems

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Zhenghui Sha ◽  
Jitesh H. Panchal
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Choice Model ◽  
Preferential Attachment ◽  
Network Evolution ◽  
System Structure ◽  
Statistical Regression ◽  
Evolutionary Systems ◽  
Local Decision ◽  
Local Decision Making

Research in systems engineering and design is increasingly focused on complex sociotechnical systems whose structures are not directly controlled by the designers, but evolve endogenously as a result of decisions and behaviors of self-directed entities. Examples of such systems include smart electric grids, Internet, smart transportation networks, and open source product development communities. To influence the structure and performance of such systems, it is crucial to understand the local decisions that result in observed system structures. This paper presents three approaches to estimate the local behaviors and preferences in complex evolutionary systems, modeled as networks, from its structure at different time steps. The first approach is based on the generalized preferential attachment model of network evolution. In the second approach, statistical regression-based models are used to estimate the local decision-making behaviors from consecutive snapshots of the system structure. In the third approach, the entities are modeled as rational decision-making agents who make linking decisions based on the maximization of their payoffs. Within the decision-centric framework, the multinomial logit choice model is adopted to estimate the preferences of decision-making nodes. The approaches are illustrated and compared using an example of the autonomous system (AS) level Internet. The approaches are generally applicable to a variety of complex systems that can be modeled as networks. The insights gained are expected to direct researchers in choosing the most applicable estimation approach to get the node-level behaviors in the context of different scenarios.

Models of network formation

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Distribution ◽  
Network Formation ◽  
Preferential Attachment ◽  
Network Size ◽  
Large Network ◽  
Citation Networks ◽  
Airline Networks ◽  
Attachment Model ◽  
Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

scholarly journals A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4041
Author(s):  
Anca Maxim ◽  
Constantin-Florin Caruntu
Keyword(s):  
Model Predictive Control ◽  
Communication Networks ◽  
Predictive Control ◽  
Technological Development ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Test Scenario ◽  
Distributed Model Predictive Control ◽  
Multi Agent ◽  
And Performance

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

scholarly journals A Multiple Criteria Decision Making Method to Weight the Sustainability Criteria of System Selection for Surface Mining

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3066
Author(s):  
Michał Patyk ◽  
Przemysław Bodziony ◽  
Zbigniew Krysa
Keyword(s):  
Decision Making ◽  
Performance Assessment ◽  
Multiple Criteria Decision Making ◽  
Surface Mining ◽  
Open Pit ◽  
Mining Equipment ◽  
Surface Mines ◽  
Mining Conditions ◽  
And Performance ◽  
Selection Of

Selection and assessment of mining equipment used in open pit rock mines relies chiefly on estimates of overall exploitation cost. The rational arrangement of mining equipment and systems comprising loading machines, haul trucks and crushing plants should be preceded by a thorough analysis of technical and economic aspects, such as investment outlays and the costs of further exploitation, which largely determine the costs of mining operations and the deposit value. Additionally, the operational parameters of the mining equipment ought to be considered. In this study, a universal set of evaluation criteria has been developed, and an evaluation method has been applied for the selection of surface mining equipment and the processing system to be operated in specific mining conditions, defined by the user. The objective of this study is to develop and apply the new methodology of multi-criteria selection of open pit rock mining equipment based on multiple criteria decision-making (MCDM) procedures, to enable the optimization of loading, handling and crushing processes. The methodology, underpinned by the principles of MCDM, provides the dedicated ranking procedures, including the ELECTRE III. The applied methodology allows the alternative options (variants) to be ranked accordingly. Ultimately, a more universal methodology is developed, applicable in other surface mines where geological and mining conditions are similar. It may prove particularly useful in selection and performance assessment of mining equipment and process line configurations in mining of low-quality rock deposits. Therefore, we undertook to develop universal criteria and applications for the selection and performance assessment of process machines for surface mines, taking into account environmental aspects as well as deposit quality.

scholarly journals CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

2021 ◽  
Vol 13 (7) ◽  
pp. 177
Author(s):  
Grazia Veronica Aiosa ◽  
Barbara Attanasio ◽  
Aurelio La Corte ◽  
Marialisa Scatá
Keyword(s):  
Communication Networks ◽  
Systems Approach ◽  
Evaluation Model ◽  
Service Evaluation ◽  
Important Change ◽  
Supplementary Information ◽  
Analytical Scheme ◽  
Evaluation Scheme ◽  
Reducing Costs ◽  
And Performance

The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document.

Sign in / Sign up

Export Citation Format

Share Document