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Destinations have in the scholarly literature been labeled as communities of interdependent organizations that collectively coproduce a variety of products and services. The paradigm comes close to describing destinations as firms which are embedded in interfirm networks. Recent studies provide crucial insights into an understanding of destinations' orchestration and structuration as coproducing interfirm networks. However, systematic knowledge about how these systems evolve and develop is lacking. This chapter addresses this issue and elaborates how the concepts of scale-free and small-world networks together can explain the process of destination evolution. The discussion also suggests how such theorizing can spur avenues for future research.

There are many complex networks like World-Wide Web, internet and social networks have been reported to be scale-free. The major property of scale-free networks is their degree distributions are in power law form. Generally, the degree exponents of scale-free networks fall into the range of (2, 3). The purpose of this paper is to investigate other situations where the degree exponents may lie outside the range.

In this paper, analysis has been carried out by varying the degree exponents in the range of (0.5, 4.5). In total, 243 scenarios have been generated with varying network size of 1,000, 2,000 and 4,000, and degree exponents in the range of (0.5, 4.5) using interval of 0.05.

The following five indicators have been investigated: average density, average clustering coefficient, average path length, average diameter and average node degree. These indicators vary with the network size and degree exponent. If certain indicators do not satisfy with the user requirement using degree exponents of (2, 3), one can further increase or decrease the value with tradeoff. Results recommend that for degree exponents in (0.5, 2), 26 possible scale-free networks can be selected whereas for (3, 4.5), 41 possible scale-free networks can be selected, assuming a 100 percent deviation on the network parameters.

A tolerance analysis is given for the tradeoff and guideline is drawn to help better design of scale-free network for degree exponents in range of (0.5, 2) and (3, 4.5) using network size 1,000, 2,000 and 4,000. The methodology is applicable to any network size.

The present study increases our understanding of strong power in exchange networks by examining its incidence in complex networks for the first time and relating this incidence to characteristics of these networks. A theoretical analysis based on network exchange theory (e.g., Willer, 1999) suggests two network characteristics predicting strong power; actors with only one potential exchange partner, and the absence of triangles, that is, one's potential exchange partners are not each other's partners. Different large-scale structures such as trees, small worlds, buyer–seller, uniform, and scale-free networks are shown to differ in these two characteristics and are therefore predicted to differ with respect to the incidence of strong power. The theoretical results and those obtained by simulating networks up to size 144 show that the incidence of strong power mainly depends on the density of the network. For high density no strong power is observed in all but buyer–seller networks, whereas for low density strong power is frequent but dependent on the large-scale structure and the two aforementioned network characteristics.

The purpose of this research is to empirically analyse the structure of the Web 2.0 service network and the mechanism behind its evolution over time.

Based on the list of Web 2.0 services and their mashups that is provided on Programmableweb, a network of Web 2.0 services was constructed. Within this network a node represents a Web 2.0 service with an open API, and a link between two nodes represents the existence of a mashup service that uses the two nodes.

The findings suggest that the evolution of the Web 2.0 service network follows the preferential attachment rule although the exponent of the preferential attachment is lower than for other networks following a preferential attachment rule. Additionally the results indicate that the Web 2.0 service network evolves to a scale‐free network but the exponent of the power law distribution is lower than for other networks.

The research applied social network analysis to the Web 2.0 service network. It showed that its network structure and the evolution mechanism are different from those found in similar areas, e.g. the world wide web (WWW). The findings imply that there are factors which lower the exponent of the preferential attachment equation and the power law distribution of the degree centralities.

This paper did not investigate the factors responsible for the low values of the exponent of the preferential attachment equation and the exponent of the power law distribution. However, it is suggested that it could be correlated with the fact that the interconnection between nodes depends on the property of the nodes.

The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus.

Inspired by the idea of network partition, taking two optimization targets which are the scale of sub-network and the sum of the strengths of the sub-network's nodes into account at the same time, a new immunization strategy based on greedy algorithm in the scale-free network is presented. After specifying the number of nodes through the immunization, the network is divided into the scale of sub-network and the sum of the strength of the sub-network's nodes as small as possible.

The experimental results show that the proposed algorithm has the better performance than targeted immunization which is supposed to be highly efficient at present.

This paper proposes a new immunization strategy based on greedy algorithm in the scale-free network for effectively solving the control of the spread of the virus.

The purpose of this paper is to advance supply chain network theory by applying theoretical and empirical developments in complex network literature to the context of supply chains as complex adaptive systems. The authors synthesize these advancements to gain an understanding of the network properties underlying efficient supply chains. To develop a suitable theory of supply chain networks, the authors look to mirror the properties of complex network models with real‐world supply chains.

The authors review complex network literature drawn from multiple disciplines in top scientific journals. From this interdisciplinary review a series of propositions are developed around supply chain complexity and adaptive phenomena.

This paper proposes that the structure of efficient supply chains follows a “scale‐free” network. This proposal emerges from arguments that the key properties of efficient supply chains are a short characteristic path length, a high clustering coefficient and a power law connectivity distribution.

The authors' discussion centres on applying advances found in recent complex network literature. Hence, the need is noted to empirically validate the series of propositions developed in this paper in a supply chain context.

If efficient supply chains resemble a scale‐free network, then managers can derive a number of implications. For example, supply chain resilience is derived by the presence of hub firms. To reduce the vulnerability of supply chains to cascading failures, it is recognized that managers could build in redundancy, undertake a multi‐sourcing strategy or intermediation between hub firms.

This paper advances supply chain network theory. It offers a novel understanding of supply chains as complex adaptive systems and, in particular, that efficient and resilient supply chain systems resemble a scale‐free network. In addition, it provides a series of propositions that allow modelling and empirical research to proceed.

The purpose of this paper is to study the failures spread in complex power grids, and what topology of power grids is best for preventing or reducing blackouts.

Based on the study of cascading failure models of complex power networks, an extended dynamical cascading failure model is proposed. Based on this model, two representatives of the complex power grids, the small‐world network and the scale‐free network, were simulated for line cascading failure. The power loss caused by cascading failures and the spreading speed of cascading failure are discussed.

Power loss caused by cascading failures in the small‐world network is much larger than that in the scale‐free network, and the speed of cascading failure propagation in the small‐world network is much faster than that in the scale‐free network.

The establishment of the dynamical cascading failure model considering other protection devices needs further study.

The results of this study can be beneficial in system planning and upgrading.

An extended dynamical cascading failure model is proposed and cascading failures in different topology of power grid are discussed.

The purpose of this paper is to present a P2P network security pricing model that promotes more secure online information sharing in P2P networks through the creation of networks with increased resistance to malicious code propagation. Online information sharing is at an all‐time high partly due to the recent growth in, and use of, online peer‐to‐peer (P2P) networks.

The model integrates current research findings in incentive compatible network pricing with recent developments in complex network theory. File download prices in P2P networks are linked to network security using a graph theory measurement called the Pearson coefficient. The Pearson coefficient indicates a structural dimension of scale‐free networks (scale‐free networks like the internet) called preferential attachment. Preferential attachment refers to the network property where the probability for a node to connect to a new node is greater if the new node already has a high number of connections.

The P2P network security pricing model concept is illustrated to show how the model functions to create more secure P2P networks.

Future research in P2P network security pricing should focus on testing the model presented in this paper by numerical experiments and simulation including the tracking of malicious code propagation on networks grown under the pricing model.

The P2P network security pricing model demonstrated here is a different approach to network security that has a strong potential to impact on the future security of P2P and other computer based networks.

The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found.

In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.

Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks.

The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

Purpose – To present the connection between modern network theory and Hayek's ideas on the brain and spontaneous orders.

Methodology/approach – To show that Hayek's ideas on the brain, spontaneous order, and why socialism cannot work are confirmed by network and self-organization theory, and to use network and self-organization theory to bridge Hayek's theory of the mind to his work on spontaneous orders.

Findings – Spontaneous orders are scale-free networks, but humans evolved a preference for hierarchical networks, which are typical of tribes and firms – and socialism. However, hierarchies only work for teleological organizations, not for ateleological spontaneous orders like economies. Part of the human preference for human-organized networks comes from our “intentional stance,” which automatically sees patterns as evidence of an organizer.

Research limitations/implications – This work acts as an introduction to possible directions in spontaneous order research. New work in bridging evolutionary and cognitive psychology (which includes Hayek's work) with self-organization and network theory acts as a promising development for neuro-Hayekians.

Social implications – Understanding there is an evolutionary bias for certain kinds of networks, even though those are not appropriate for certain kinds of social orders, and understanding the nature of these networks should help us understand the true relationships among individuals, organizations, and spontaneous orders.

Originality/value of chapter – This work brings Hayek “up to date,” with network theory and self-organization, showing to what extent Hayek was talking about these concepts. Seeing the similarities and differences between hierarchical and scale-free networks helps one understand how they come about, and in what contexts.