scholarly journals Improved Degree Search Algorithms in Unstructured P2P Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Guole Liu ◽  
Haipeng Peng ◽  
Lixiang Li ◽  
Yixian Yang ◽  
Qun Luo
Keyword(s):  
Success Rate ◽  
Search Algorithm ◽  
Memory Function ◽  
Small World ◽  
Search Algorithms ◽  
P2p Networks ◽  
Small World Networks ◽  
Breadth First Search ◽  
Network Resources ◽  
Scale Free

Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P) networks. Breadth-first search (BFS) and depth-first search (DFS) are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD) and memory function preference degree algorithm (PD). We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.

Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains

2013 ◽  
Vol 110 (10) ◽  
Author(s):  
Ralph Stoop ◽  
Victor Saase ◽  
Clemens Wagner ◽  
Britta Stoop ◽  
Ruedi Stoop
Keyword(s):  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Cortical Columns

scholarly journals Quantitative method for resilience assessment framework of airport network during COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260940
Author(s):  
Jiuxia Guo ◽  
Yang Li ◽  
Zongxin Yang ◽  
Xinping Zhu
Keyword(s):  
Complex Network ◽  
Small World ◽  
Global Scale ◽  
Assessment Framework ◽  
Small World Networks ◽  
Scale Free ◽  
Global Pandemic ◽  
Network Metrics ◽  
Resilience Assessment

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

Complex Systems in a Nutshell

2020 ◽  
pp. 19-32
Author(s):  
Megan S. Patterson ◽  
Michael K. Lemke ◽  
Jordan Nelon
Keyword(s):  
Complex Systems ◽  
Path Length ◽  
Path Dependence ◽  
Small World ◽  
Cardiometabolic Disease ◽  
Small World Networks ◽  
Stable States ◽  
Scale Free ◽  
Critical Transitions ◽  
Random Scale

This chapter provides an overview of the key foundational concepts and principles of the study of complex systems. First, a definition for system is provided, and the distinctions between complicated and complex systems are demarcated, as are detail, disorganized, organized, and dynamic types of complexity. Common properties across complex systems are defined and described, including stable states and steady states, path dependence, resilience, critical transitions and tipping points, early warning signals, feedback loops, and nonlinearity. This chapter also delves into how complex issues often consist of networks, with random, scale-free, and small world networks defined and network concepts such as degrees, path length, and heterogeneity defined. The concept of emergence is also emphasized, as well as related principles such as adaptation and self-organization. Cardiometabolic disease (and associated comorbidities) is used in this chapter as a thematic population health example.

Propagation and stability in software: A complex network perspective

2015 ◽  
Vol 26 (05) ◽  
pp. 1550052 ◽  
Author(s):  
Lei Wang ◽  
Ping Wang
Keyword(s):  
Software Engineering ◽  
Complex Networks ◽  
Large Scale ◽  
Structural Difference ◽  
Small World ◽  
Change Propagation ◽  
Small World Networks ◽  
Typical Structure ◽  
Scale Free ◽  
Scale Free Networks

In this paper, we attempt to understand the propagation and stability feature of large-scale complex software from the perspective of complex networks. Specifically, we introduced the concept of "propagation scope" to investigate the problem of change propagation in complex software. Although many complex software networks exhibit clear "small-world" and "scale-free" features, we found that the propagation scope of complex software networks is much lower than that of small-world networks and scale-free networks. Furthermore, because the design of complex software always obeys the principles of software engineering, we introduced the concept of "edge instability" to quantify the structural difference among complex software networks, small-world networks and scale-free networks. We discovered that the edge instability distribution of complex software networks is different from that of small-world networks and scale-free networks. We also found a typical structure that contributes to the edge instability distribution of complex software networks. Finally, we uncovered the correlation between propagation scope and edge instability in complex networks by eliminating the edges with different instability ranges.

Complex networks

2018 ◽  
Author(s):  
Graziano Vernizzi ◽  
Henri Orland
Keyword(s):  
Complex Networks ◽  
Small World ◽  
Laplacian Matrix ◽  
Square Lattice ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Scale Free Graphs ◽  
Regular Networks

This article deals with complex networks, and in particular small world and scale free networks. Various networks exhibit the small world phenomenon, including social networks and gene expression networks. The local ordering property of small world networks is typically associated with regular networks such as a 2D square lattice. The small world phenomenon can be observed in most scale free networks, but few small world networks are scale free. The article first provides a brief background on small world networks and two models of scale free graphs before describing the replica method and how it can be applied to calculate the spectral densities of the adjacency matrix and Laplacian matrix of a scale free network. It then shows how the effective medium approximation can be used to treat networks with finite mean degree and concludes with a discussion of the local properties of random matrices associated with complex networks.

scholarly journals Mapping Koch curves into scale-free small-world networks

2010 ◽  
Vol 43 (39) ◽  
pp. 395101 ◽  
Author(s):  
Zhongzhi Zhang ◽  
Shuyang Gao ◽  
Lichao Chen ◽  
Shuigeng Zhou ◽  
Hongjuan Zhang ◽  
...  
Keyword(s):  
Small World ◽  
Small World Networks ◽  
Scale Free

scholarly journals Resilience of Interbank Market Networks to Shocks

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Shouwei Li ◽  
Jianmin He
Keyword(s):  
Network Models ◽  
Small World ◽  
Interbank Market ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Tiered Networks ◽  
The Stability ◽  
Random Shocks ◽  
Market Networks

This paper first constructs a tiered network model of the interbank market. Then, from the perspective of contagion risk, it studies numerically the resilience of four types of interbank market network models to shocks, namely, tiered networks, random networks, small-world networks, and scale-free networks. This paper studies the interbank market with homogeneous and heterogeneous banks and analyzes random shocks and selective shocks. The study reveals that tiered interbank market networks and random interbank market networks are basically more vulnerable against selective shocks, while small-world interbank market networks and scale-free interbank market networks are generally more vulnerable against random shocks. Besides, the results indicate that, in the four types of interbank market networks, scale-free networks have the highest stability against shocks, while small-world networks are the most vulnerable. When banks are homogeneous, faced with selective shocks, the stability of the tiered interbank market networks is slightly lower than that of random interbank market networks, whereas, in other cases, the stability of the tiered interbank market networks is basically between that of random interbank market networks and that of scale-free interbank market networks.

scholarly journals Concurrent scale-free and small-world networks support criticality in cortical ensembles

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Paolo Massobrio ◽  
Valentina Pasquale ◽  
Sergio Martinoia
Keyword(s):  
Small World ◽  
Small World Networks ◽  
Scale Free

Strategy Study on Maximum Degree and Bisection Degree Synchronization Search in Scale-Free Networks

2013 ◽  
Vol 303-306 ◽  
pp. 2157-2160
Author(s):  
Xin Yi Chen ◽  
Jian Hua Xia
Keyword(s):  
Maximum Degree ◽  
Search Strategy ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Search Efficiency ◽  
Network Congestion ◽  
Breadth First Search ◽  
Strategy Study ◽  
Scale Free ◽  
Search Speed

With the expansion of network and the increasing number of communities’ network, It’s a big problem for the search algorithm to enhance the search efficiency. The number of search steps and the amount of query information generated by maximum degree search strategy, which will grow exponentially, consequently, and lead to low the efficiency of search. Without considering the network congestion, breadth-first search strategy is undoubtedly the best search efficiency. From the point of the breadth-first search strategy, this paper designed and proposed the synchronous search strategy of Maximum degree and Bisection degree, and described the algorithm idea and algorithm design for MBDS. The simulation results showed that MBDS not only decreased the amount of query information, but also can efficiently decrease the search steps and improve the search speed.

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