Greedy Forwarding in Dynamic Scale-Free Networks Embedded in Hyperbolic Metric Spaces

We show that complex (scale-free) network topologies naturally emerge from hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these networks. Greedy forwarding is topology-oblivious. Nevertheless, greedy packets find their destinations with 100% probability following almost optimal shortest paths. This remarkable efficiency sustains even in highly dynamic networks. Our findings suggest that forwarding information through complex networks, such as the Internet, is possible without the overhead of existing routing protocols, and may also find practical applications in overlay networks for tasks such as application-level routing, information sharing, and data distribution

International Entry Decision for Design Firms

International projects within the Architecture-Engineering-Construction (AEC) sector have increased both in number and in revenue during the past two decades. AEC companies seek projects outside of their home country at much higher rates than the past and the decision to enter into a new market is one of the most critical decisions AEC companies face in often volatile and competitive environments. There are few studies that investigate the important factors influencing international entry into a new market. This dissertation developed a model investigating the influence of two company specific factors, international experience and embeddedness, and two country institutions, legal system and corruption, on the entry in a new international market using event history analysis. The focus of this study is to analyze the entry decision making for firms that are working in the Architecture and Engineering sector of the construction industry. In this dissertation these companies are classified as Design Firms. The logit regression model was developed to understand the influence of four dependent variables on the entry decision of design firms. The model controls for GDP per capita, market competition, and diversification level of companies. The analysis was based on the longitudinal data from international design firms entering in the Central Eastern European countries since 1991 when the Soviet Union sphere of influence waned. The results of this study contribute to the body of knowledge by introducing a quantitative model that investigates the influence of company and country factors on the international entry of design firms. Practitioners can use the results of this study in their entry decision-making. The results may also help practitioners identify and collect important information and knowledge as they pursue international projects

Curvature and temperature of complex networks

We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the Internet into the hyperbolic plane, and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of Internet routing

The Internet AS-Level Topology: Three Data Sources and One Definitive Metric

We calculate an extensive set of characteristics for Internet AS topologies extracted from the three data sources most frequently used by the research community: traceroutes, BGP, and WHOIS. We discover that traceroute and BGP topologies are similar to one another but differ substantially from the WHOIS topology. Among the widely considered metrics, we find that the joint degree distribution appears to fundamentally characterize Internet AS topologies as well as narrowly define values for other important metrics. We discuss the interplay between the specifics of the three data collection mechanisms and the resulting topology views. In particular, we show how the data collection peculiarities explain differences in the resulting joint degree distributions of the respective topologies. Finally, we release to the community the input topology datasets, along with the scripts and output of our calculations. This supplement should enable researchers to validate their models against real data and to make more informed selection of topology data sources for their specific needs.Comment: This paper is a revised journal version of cs.NI/050803

Systematic Topology Analysis and Generation Using Degree Correlations

We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex representation of the probability distribution. Using this series, we can quantitatively measure the distance between two graphs and construct random graphs that accurately reproduce virtually all metrics proposed in the literature. The nature of the dK-series implies that it will also capture any future metrics that may be proposed. Using our approach, we construct graphs for d=0,1,2,3 and demonstrate that these graphs reproduce, with increasing accuracy, important properties of measured and modeled Internet topologies. We find that the d=2 case is sufficient for most practical purposes, while d=3 essentially reconstructs the Internet AS- and router-level topologies exactly. We hope that a systematic method to analyze and synthesize topologies offers a significant improvement to the set of tools available to network topology and protocol researchers.Comment: Final versio