The treatment of Internet traffic is increasingly affected by national policies that require the ISPs in a country to adopt common protocols or practices. Examples include government enforced censorship, wiretapping, and protocol deployment mandates for IPv6 and DNSSEC. If an entire nation's worth of ISPs apply common policies to Internet traffic, the global implications could be significant. For instance, how many countries rely on China or Great Britain (known traffic censors) to transit their traffic? These kinds of questions are surprisingly difficult to answer, as they require combining information collected at the prefix, Autonomous System, and country level, and grappling with incomplete knowledge about the AS-level topology and routing policies. In this paper we develop the first framework for country-level routing analysis, which allows us to answer questions about the influence of each country on the flow of international traffic. Our results show that some countries known for their national policies, such as Iran and China, have relatively little effect on interdomain routing, while three countries (the United States, Great Britain, and Germany) are central to international reachability, and their policies thus have huge potential impact.
Finally, there has been an enormous amount of work developing statistical measures of network properties @cite_24 @cite_1 @cite_13 , including preferential attachment models @cite_17 and many models of the AS network @cite_9 @cite_6 @cite_7 @cite_35 @cite_22 . Some of this work measures node centrality by the impact it would have on network connectivity if the AS was deleted, known as deletion impact @cite_11 @cite_6 . A parallel can potentially be made between node deletion and censorship. For example, deleting a country from the network is conceptually similar to all other ASes collectively routing around that country.
@cite_35 @cite_11 @cite_22 @cite_7 @cite_9 @cite_1 @cite_6 @cite_24 @cite_13 @cite_17
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Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis. © 2009 Wiley Periodicals, Inc. Abstract As network analysis methods prevail, more metrics are applied to co-word networks to reveal hot topics in a field. However, few studies have examined the relationships among these metrics. To bridge this gap, this study explores the relationships among different ranking metrics, including one frequency-based and six network-based metrics, in order to understand the impact of network structural features on ranking themes on co-word networks. We collected bibliographic data from three disciplines from Web of Science (WoS), and generated 40 simulation networks following the preferential attachment assumption. Correlation analysis on the empirical and simulated networks shows strong relationships among the metrics. Their relationships are consistent across disciplines. The metrics can be categorized into three groups according to the strength of their correlations, where Degree Centrality, H-index, and Coreness are in one group, Betweenness Centrality, Clustering Coefficient, and frequency in another, and Weighted PageRank by itself. Regression analysis on the simulation networks reveals that network topology properties, such as connectivity, sparsity, and aggregation, influence the relationships among selected metrics. In addition, when comparing the top keywords ranked by the metrics in the three disciplines, we found the metrics exhibit different discriminative capacity. Coreness and H-index may be better suited for categorizing keywords rather than ranking keywords. Findings from this study contribute to a better understanding of the relationships among different metrics and provide guidance for using them effectively in different contexts. An analysis is conducted on the robustness of measures of centrality in the face of random error in the network data. We use random networks of varying sizes and densities and subject them (separately) to four kinds of random error in varying amounts. The types of error are edge deletion, node deletion, edge addition, and node addition. The results show that the accuracy of centrality measures declines smoothly and predictably with the amount of error. This suggests that, for random networks and random error, we shall be able to construct confidence intervals around centrality scores. In addition, centrality measures were highly similar in their response to error. Dense networks were the most robust in the face of all kinds of error except edge deletion. For edge deletion, sparse networks were more accurately measured. We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.