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The purpose of this paper is to explore the evolving mechanism of urban roadway network. With the consideration of self-organization effect and planning effect during evolution, the authors try to demonstrate the impact of preferential attachment, module scale and module structure on the evolving network model.

The roadway network is built in the form of abstract network by dual approach. By using the evolving model of modular growth, the authors analyze the effects and mechanism of the evolving process. Then through numerical analysis, the impact of evolving effects on urban roadway network topology structure is discussed from the aspects of preferential attachment, module scale and module structure.

The module structure property, small-world property and scale-free property of roadway network can be affected with various degrees by the change of preferential attachment and module scale. However, the impact of module structure on network properties is small, which can be ignored. Therefore, in practice, the self-organization effect and planning effect of evolving network can be reached by changing the preferential attachment and module scale, so as to generate the network structure with specific properties.

Some local events, such as road extensions, road demolition and intersection rebuilding, exist during the evolving process under real-world situation. While those cases have not been considered in preferential attachment. Therefore, researchers are encouraged to take these factors into consideration in further research.

The paper has implications for practice in urban transportation planning and roadway constructions, which can help to guide the planning of urban roadway and to adjust or restore partial network when broken down according to the evolving law.

The impact of preferential attachment, module scale and module structure on the evolving network model is measured. And the relationship between different network properties can be used to build some patterns of network. From this point of view, the development of urban roadway network can be predicted and intervened.

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 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.

What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis.

This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network.

This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies.

The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.

This paper aims to analyze the role played by two dimensions of entrepreneurs' private social capital in the survival, growth and innovativeness of entrepreneurial service ventures: local size and preferential attachment degree.

Data were collected by a questionnaire, the unit of investigation being the private entrepreneur in the service sector in the city of Shanghai. The questionnaire allows the authors to identify the social network of the entrepreneurs, estimate the empirical degree distribution for the entire sample, and estimate local size and preferential attachment degree.

There is empirical evidence that entrepreneurs do not create social networks with preferential attachment structure and a large local size of the network increases the chances of survival of new ventures; however, the chance to become a dynamic venture is only related to entrepreneurs' preferential attachment degree.

Any entrepreneurial strategy should include an investment plan to generate a minimum level of social capital or guanxi. Additionally, efficient strategy to generate social capital for dynamic entrepreneurship should focus on the creation of quality social capital. The underestimation of the role of social capital seems to have a deeper impact when analyzing the phenomenon of entrepreneurial innovation.

This conclusion suggests that the role of social capital in the entrepreneurial process could be underestimated systematically, since most of the literature only considers local measures of social capital. This underestimation seems to have a deeper impact when analyzing the phenomenon of entrepreneurial innovation.

This study aims to not only develop measurements of preferential attachment and homophily mechanisms based on their definitions and network theory but also examine the associations among these network mechanisms, community commitment, knowledge sharing and community citizenship behavior.

In total, 250 valid questionnaires are collected to examine the hypothesized associations. These hypotheses are examined by using partial least squares structural equation modeling.

The findings reveal both mechanisms are beneficial to develop new entrants’ emotional attachment to a virtual community, thereby motivating knowledge sharing and community altruistic behavior. The results contribute some practical and theoretical implications that are very helpful for the conceptualization of network mechanisms, community development, relationship management and incentives for extra-role behavior.

The literature on the link between network selection mechanisms and knowledge sharing remains unknown. This study is the pioneer to disclose this unknown association and examine the impacts of preferential attachment and homophily network mechanisms.

To exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors’ community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP.

In this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity.

The authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network.

The study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.

Inter-organizational alliances and the networks they generate have been a central topic in organization theory over the last decade. However, network analyses per se have been static. Even when information over time has been available, the temporal component has been set aside or aggregated to the end point of the study. Substantially more research has been conducted on organizations initiating inter-organizational relationships. The organization-level research has been decidedly dynamic in nature. However, organization-level research has largely examined the structural characteristics of the networks generated by organizational actions. Work combining network-level and organization-level phenomena has been rare and, to our knowledge, no research including the effects of organization-level actions on the evolution of network-level phenomena has occurred.

In this chapter we use more than 6000 R&D alliances and more than 6500 M&D alliances initiated by more than 1000 biotech firms in the U.S. over a 30 year period to construct quarterly networks. We test 13 hypotheses linking the actions of the firms to changes in network structure. Utilizing hazard-rate models we test the effects of institutional status, positional status (centrality), and structural status (coreness) of firms on their propensity to form ties with different structural consequences. Our research indicates that both R&D and M&D networks in U.S. biotechnology are developing a distinct core/periphery structure over time. Furthermore, we find support for a process of preferential attachment wherein organizations are more likely to form ties with organizations of similar institutional and structural status. Furthermore, we find evidence for cross effects, for example attachment processes that enfold across the two networks.

This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network.

Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network.

The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm.

By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them.

It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.