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相关论文: Accurately modeling the Internet topology

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The internet structure is extremely complex. The Positive-Feedback Preference (PFP) model is a recently introduced internet topology generator. The model uses two generic algorithms to replicate the evolution dynamics observed on the…

网络与互联网体系结构 · 计算机科学 2007-05-23 Shi Zhou

To study large-scale effects on the Internet various mod- els have been introduced to generate Internet-like autono- mous system (AS) topologies. The models for large-scale AS topologies have been focused on replicating structural graph…

网络与互联网体系结构 · 计算机科学 2014-09-25 Arne Schwabe , Holger Karl

We present an analytically tractable model of Internet evolution at the level of Autonomous Systems (ASs). We call our model the multiclass preferential attachment (MPA) model. As its name suggests, it is based on preferential attachment.…

网络与互联网体系结构 · 计算机科学 2010-04-13 Srinivas Shakkottai , Marina Fomenkov , Ryan Koga , Dmitri Krioukov , kc claffy

We present the first complete measurement of the Chinese Internet topology at the autonomous systems (AS) level based on traceroute data probed from servers of major ISPs in mainland China. We show that both the Chinese Internet AS graph…

网络与互联网体系结构 · 计算机科学 2007-08-08 Shi Zhou , Guo-Qiang Zhang , Guo-Qing Zhang

The Internet topology is of high importance in designing networks and architectures, evaluating performance, and economics. Interconnections between domains (ASes), routers, and points of presence (PoPs), have been measured, analyzed, and…

网络与互联网体系结构 · 计算机科学 2017-06-27 Pavlos Sermpezis , George Nomikos , Xenofontas Dimitropoulos

The Internet topology at the Autonomous Systems level (AS graph) has a power--law degree distribution and a tier structure. In this paper, we introduce the Interactive Growth (IG) model based on the joint growth of new nodes and new links.…

网络与互联网体系结构 · 计算机科学 2008-12-15 Shi Zhou , Raul J. Mondragon

A key ingredient of current models proposed to capture the topological evolution of complex networks is the hypothesis that highly connected nodes increase their connectivity faster than their less connected peers, a phenomenon called…

统计力学 · 物理学 2009-11-07 H. Jeong , Z. Neda , A. -L. Barabasi

There are diverse mechanisms driving the evolution of social networks. A key open question dealing with understanding their evolution is: How various preferential linking mechanisms produce networks with different features? In this paper we…

物理与社会 · 物理学 2015-06-12 Haibo Hu , Jinli Guo , Xuan Liu

Preferential attachment is a popular model of growing networks. We consider a generalized model with random node removal, and a combination of preferential and random attachment. Using a high-degree expansion of the master equation, we…

统计力学 · 物理学 2012-01-20 Heiko Bauke , Cristopher Moore , Jean-Baptiste Rouquier , David Sherrington

Researchers have proposed a variety of Internet topology models. However almost all of them focus on generating one graph based on one single static source graph. On the other hand, Internet topology is evolving over time continuously with…

网络与互联网体系结构 · 计算机科学 2008-12-31 Lian-dong Liu , Ke Xu

This paper presents a statistically sound method for measuring the accuracy with which a probabilistic model reflects the growth of a network, and a method for optimising parameters in such a model. The technique is data-driven, and can be…

网络与互联网体系结构 · 计算机科学 2009-04-07 Richard Clegg , Raul Landa , Uli Harder , Miguel Rio

Popularity is attractive -- this is the formula underlying preferential attachment, a popular explanation for the emergence of scaling in growing networks. If new connections are made preferentially to more popular nodes, then the resulting…

Networks such as organizational network of a global company play an important role in a variety of knowledge management and information diffusion tasks. The nodes in these networks correspond to individuals who are self-interested. The…

计算机科学与博弈论 · 计算机科学 2014-10-10 Swapnil Dhamal , Y. Narahari

Reciprocity, or the tendency of individuals to mirror behavior, is a key measure that describes information exchange in a social network. Users in social networks tend to engage in different levels of reciprocal behavior. Differences in…

机器学习 · 统计学 2023-08-22 Daniel Cirkovic , Tiandong Wang

Relevance and importance are the main factors when humans build network connections. We propose an evolutionary network model based on preferential attachment(PA) considering these factors. We analyze and compute several important features…

物理与社会 · 物理学 2014-12-25 Weituo Zhang , Chjan Lim

The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in…

网络与互联网体系结构 · 计算机科学 2009-11-02 Xenofontas Dimitropoulos , Dmitri Krioukov , Amin Vahdat , George Riley

This paper expands the degree-based consideration of the preferential attachment growth process and applies five different connectivity criteria (node degree, clustering coefficient, betweenness centrality, closeness centrality, and…

物理与社会 · 物理学 2020-01-16 Dimitrios Tsiotas

Inferring the network topology from the dynamics is a fundamental problem with wide applications in geology, biology and even counter-terrorism. Based on the propagation process, we present a simple method to uncover the network topology.…

物理与社会 · 物理学 2013-11-21 An Zeng

The models of the Internet reported in the literature are mainly aimed at reproducing the scale-free structure, the high clustering coefficient and the small world effects found in the real Internet, while other important properties (e.g.…

物理与社会 · 物理学 2011-11-10 F. A. Rodrigues , P. R. Villas Boas , G. Travieso , L. da F. Costa

We present an analysis of the topologies of a class of networks which are optimal in terms of the requirements of having as short a route as possible between any two nodes while yet keeping the congestion in the network as low as possible.…

统计力学 · 物理学 2009-11-10 Vittoria Colizza , Jayanth R. Banavar , Amos Maritan , Andrea Rinaldo
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