Analysis of complex network performance and heuristic node removal strategies

Jahanpour, Ehsan; Chen, Xin

doi:10.1016/j.cnsns.2013.04.030

Cited by 31 publications

(15 citation statements)

References 17 publications

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“…It must be remarked that the responses to node removal differ from system to system and many complex networks display different levels of vulnerability against node removals. A centrality which is optimal for one application is sometimes sub-optimal for a different application, and it is impossible to find a universal index that best quantifies nodes' importance in every situation [6,29,70]. In order to seek more universal conclusions, we will conduct our investigations on more empirical and theoretical networks in the future.…”

Section: Discussionmentioning

confidence: 99%

Node Importance Ranking of Complex Networks with Entropy Variation

2017

Entropy

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The heterogeneous nature of a complex network determines the roles of each node in the network that are quite different. Mechanisms of complex networks such as spreading dynamics, cascading reactions, and network synchronization are highly affected by a tiny fraction of so-called important nodes. Node importance ranking is thus of great theoretical and practical significance. Network entropy is usually utilized to characterize the amount of information encoded in the network structure and to measure the structural complexity at the graph level. We find that entropy can also serve as a local level metric to quantify node importance. We propose an entropic metric, Entropy Variation, defining the node importance as the variation of network entropy before and after its removal, according to the assumption that the removal of a more important node is likely to cause more structural variation. Like other state-of-the-art methods for ranking node importance, the proposed entropic metric is also used to utilize structural information, but at the systematical level, not the local level. Empirical investigations on real life networks, the Snake Idioms Network, and several other well-known networks, demonstrate the superiority of the proposed entropic metric, notably outperforming other centrality metrics in identifying the top-k most important nodes.Entropy 2017, 19, 303 2 of 17 ranking important nodes make use of the structural information [6]. For more details about the above mentioned centralities, we refer to [6,29].Structural complexity is perhaps the most important property of a complex network [1]. Network entropy is usually utilized to characterize the amount of information encoded in the network structure and to measure the structural complexity [30]. Recently, considerable effort has focused on quantifying network complexity using entropy measures [31], and several entropic metrics have been proposed; to name a few, network connectivity entropy [32], cyclic entropy [33], mapping entropy [34], hotspot entropy [35], Riemannian-geometric entropy [36], and q-entropy [37]. These measures have been shown to be extremely successful in quantifying the level of organization encoded in structural features of networks.As was mentioned before, we often resort to network structural information for ranking node importance, while entropy can serve as a fundamental tool to capture the structural information of complex networks. However, to the best of our knowledge, there are seldom entropic metrics developed to measure node importance. Two recent exceptions are the Relative Entropy [38] and Expected Force [39]. Relative entropy [38] is proposed as an integrated evaluation approach for node importance, which generates an optimal solution from different individual centrality indices by linear programming. Thus relative entropy serves not as a direct metric for node importance ranking, but a hybrid approach for synthesizing the existing ones. Expected Force [39] is a node property derived from local network topology to depict the ...

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Section: Discussionmentioning

confidence: 99%

Node Importance Ranking of Complex Networks with Entropy Variation

2017

Entropy

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“…As second objective function F 2 , we adopt a complex network metric, specifically the Average Node Coverage (ANC) introduced [52] as a new network diffusion speed metric describing how fast information (or entities) are distributed from a node (center) to other nodes (periphery). It computes the average number of nodes that receive information (or entities) in one time unit, given that all connected nodes receive information (or entities) and it takes one time unit for a node to pass information to all its directly connected nodes.…”

Section: Objective Functionsmentioning

confidence: 99%

A complex network theory approach for optimizing contamination warning sensor location in water distribution networks

Nazempour

Monfared

Zio

2018

International Journal of Disaster Risk Reduction

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Drinking water for human health and well-being is crucial. Accidental and intentional water contamination can pose great danger to consumers. Optimal design of a system that can quickly detect the presence of contamination in a water distribution network is very challenging for technical and operational reasons. However, on the one hand improvement in chemical and biological sensor technology has created the possibility of designing efficient contamination detection systems. On the other hand, methods and tools from complex network theory, which was primarily the domain of mathematicians and physicists, provide analytical output for engineers to design, optimize, operate, and maintain complex network systems such as power grids, water distribution networks, telecommunication systems, internet, roads, supply chains, traffic and transportation systems.In this work, we develop a new modeling approach for the optimal placement of sensors for contamination detection in a water distribution network. The approach originally combines classical optimization and complex systems theory.

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“…With the development of technology, it is feasible to mine information in complex networks. The important nodes in the network community are the nodes that have an important impact on the network, which has very important practical significance for controlling rumor spread, the spread of infectious diseases [2], traffic conditions [3], etc. This is why the recognition algorithm of important nodes has been widely researched.…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition Algorithm of important Nodes in Complex Network

Su¹

2020

IJCAI

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It is crucial to identify the important nodes in the network. On the basis of the K-shell algorithm, this study researched the recognition of important nodes in complex networks. The study introduces concepts of edge weight and influence coefficient, the design of an IKS algorithm, and an analysis of its recognition effect in Zachary network and real micro blog network. The results showed that the partition results of the K-shell algorithm were coarse, while the partition results of the IKS algorithm were refined; the IKS algorithm could sort the important nodes accurately on the basis of the K-shell algorithm, and its rationality was higher than that of the closeness centralization and PaperRank algorithm. The partition results in the microblog network also verified the effectiveness of the improved method. The experimental results show that the IKS algorithm is reliable in the important node identification, which is beneficial to the recognition of important nodes in complex network. Povzetek: V študentskem prispevku je opisan študij algoritma za določitev pomembnih vozlišč v kompleksnih omrežjih.

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Analysis of complex network performance and heuristic node removal strategies

Cited by 31 publications

References 17 publications

Node Importance Ranking of Complex Networks with Entropy Variation

Node Importance Ranking of Complex Networks with Entropy Variation

A complex network theory approach for optimizing contamination warning sensor location in water distribution networks

Research on Recognition Algorithm of important Nodes in Complex Network

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