A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

Failure Mode, Effects and Criticality Analysis (FMECA) is a method which involves quantitative failure analysis. It systematically examines potential failure modes in a system, as well as the components of the system, to determine the impact of a failure. In addition, it is one of the most powerful techniques used for risk assessment and maintenance management. However, various drawbacks are inherent to the classical FMECA method, especially in ranking failure modes. This paper proposes a novel approach that uses complex networks theory to support FMECA. Firstly, the failure modes and their causes and effects are defined as nodes, and according to the logical relationship between failure modes, and their causes and effects, a weighted graph is established. Secondly, we use complex network theory to analyze the weighted graph, and the entropy centrality approach is applied to identify influential nodes. Finally, a real-world case is presented to illustrate and verify the proposed method.

Section: Proposed Methodsmentioning

confidence: 99%

A Novel Approach to Support Failure Mode, Effects, and Criticality Analysis Based on Complex Networks

Wang

Dai

Luo³

et al. 2019

“…Information entropy has also been used as importance indicator to measure nodes’ importance [ 44 , 45 ]. Qiao et al proposed entropy centrality [ 46 , 47 ] to measure the potential for communication activity between node pair. Ai assumes that the removal of a more important node is likely to cause more structural variation.…”

Section: Introductionmentioning

confidence: 99%

Influential Nodes Identification in Complex Networks via Information Entropy

Guo

Yang

Chen³

et al. 2020

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes' spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.Entropy 2020, 22, 242 2 of 19 diffusion [9], and even detect essential proteins [10]. On the other hand, by removing some critical nodes, it can greatly reduce the connectivity of the network to restrain the outbreak of epidemics [11] or spreading of rumors [12].The ongoing COVID-19 epidemics is catching wide attention around the world. Every country is making enormous effort to control the virus spreading. By analyzing social networks, it would be easier for us to control epidemics spreading. In the last decades, propagation dynamics has always been an important research direction. Many mechanisms, such as epidemic spreading [13][14][15][16], rumor propagation [17,18], social sudden events spreading [19], and e-commercial advertisements, are all closely related to complex network dynamics. Early in 1760, Daniel Bernoulli studied smallpox vaccine by using ordinary differential equations, and gave the Bernoulli equations [20] , which is one of the earliest propagation dynamics models. Later, Hamer presented the mass-action principle [21,22] when studying the recurring epidemics of measles. A.G. McKendrick and W.O. Kermack formulated a famous modern mathematical epidemic model named the Susceptible-Infected-Recovered (SIR) compartmental model when studying the spreading pattern of the Black Death and the plague in 1906. SIR compartmental model successfully predicted the outbreak of several epidemics [23]. Harding et al. [24] followed the maximum entropy (MaxEnt) principle when simulating on the SIS model to study epidemics spreading on networks. Wang et ...

“…The complexity of networks has increased exponentially. Many works have been conducted on complex networks, such as community detection [ 25 , 26 ], network controllability [ 27 , 28 ], node ranking [ 29 , 30 ], link prediction [ 31 , 32 ] and evolutionary game [ 33 , 34 ]. More and more infrastructures in modern society are showing their network characteristics.…”

Section: Introductionmentioning

confidence: 99%

Applying the Bayesian Stackelberg Active Deception Game for Securing Infrastructure Networks

Zeng

Ren

Liu

et al. 2019

With new security threats cropping up every day, finding a real-time and smart protection strategy for critical infrastructure has become a big challenge. Game theory is suitable for solving this problem, for it provides a theoretical framework for analyzing the intelligent decisions from both attackers and defenders. However, existing methods are only based on complete information and only consider a single type of attacker, which is not always available in realistic situations. Furthermore, although infrastructure interconnection has been greatly improved, there is a lack of methods considering network characteristics. To overcome these limitations, we focus on the problem of infrastructure network protection under asymmetry information. We present a novel method to measure the performance of infrastructure from the network perspective. Moreover, we propose a false network construction method to simulate how the defender applies asymmetric information to defend against the attacker actively. Meanwhile, we consider multiple types of attackers and introduce the Bayesian Stackelberg game to build the model. Experiments in real infrastructure networks reveal that our approach can improve infrastructure protection performance. Our method gives a brand new way to approach the problem of infrastructure security defense.