7007263
The rich-club phenomenon in the Internet topology
We show that the Internet topology at the autonomous system (AS) level has a rich-club phenomenon. The rich nodes, which are a small number of nodes with large numbers of links, are very well connected to each other. The rich-club is a core tier that we measured using the rich-club connectivity and the node-node link distribution. We obtained this core tier without any heuristic assumption between the ASs. The rich-club phenomenon is a simple qualitative way to differentiate between power law topologies and provides a criterion for new network models. To show this, we compared the measured rich-club of the AS graph with networks obtained using the Baraba/spl acute/si-Albert (BA) scale-free network model, the Fitness BA model and the Inet-3.0 model.
728
Analyzing and modelling the AS-level Internet topology
Recently we introduced the rich-club phenomenon as a quantitative metric to characterize the tier structure of the Autonomous Systems level Internet topology (AS graph) and we proposed the Interactive Growth (IG) model, which closely matches the degree distribution and hierarchical structure of the AS graph and compares favourble with other available Internet power-law topology generators. Our research was based on the widely used BGP AS graph obtained from the Oregon BGP routing tables. Researchers argue that Traceroute AS graph, extracted from the traceroute data collected by the CAIDA's active probing tool, Skitter, is more complete and reliable. To be prudent, in this paper we analyze and compare topological structures of Traceroute AS graph and BGP AS graph. Also we compare with two synthetic Internet topologies generated by the IG model and the well-known Barabasi-Albert (BA) model. Result shows that both AS graphs show the rich-club phenomenon and have similar tier structures, which are closely matched by the IG model, however the BA model does not show the rich-club phenomenon at all.
1466117
Redundancy and Robustness of the AS-level Internet topology and its models
A comparison between the topological properties of the measured Internet topology, at the autonomous system level (AS graph), and the equivalent graphs generated by two different power law topology generators are presented. Only one of the synthetic generators reproduces the tier connectivity of the AS graph.
10758103
Accurately modeling the Internet topology
Based on measurements of the internet topology data, we found that there are two mechanisms which are necessary for the correct modeling of the internet topology at the autonomous systems (AS) level: the interactive growth of new nodes and new internal links, and a nonlinear preferential attachment, where the preference probability is described by a positive-feedback mechanism. Based on the above mechanisms, we introduce the positive-feedback preference (PFP) model which accurately reproduces many topological properties of the AS-level internet, including degree distribution, rich-club connectivity, the maximum degree, shortest path length, short cycles, disassortative mixing, and betweenness centrality. The PFP model is a phenomenological model which provides an insight into the evolutionary dynamics of real complex networks.
3804246
The missing links in the BGP-based AS connectivity maps
PAM2003 - The Passive and Active Measurement Workshop(http://www.pam2003.org), San Diego, USA, April 2003
13207643
Towards Modelling The Internet Topology - The Interactive Growth Model
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. This simple and dynamic model compares favorable with other Internet power-law topology generators because it not only closely resembles the degree distribution of the AS graph, but also accurately matches the hierarchical structure, which is measured by the recently reported rich-club phenomenon.
8520561
Bounding the bandwidths for graphs
Let G;H be nite graphs with jV(H)j>jV(G)j. The bandwidth of G with respect to H is dened to be BH(G) = minmaxuv2E(G)dH((u);(v)), with the minimum taken over all injections from V(G )t oV(H), where dH(x;y) is the distance in H between two vertices x;y 2 V(H). This number is involved with the VLSI design and optimization, especially when the \host" graph H is a path Pn or a cycle Cn of length n = jV(G)j. In these two cases, BH(G) is known to be the ordinary bandwidth B(G) and the cyclic bandwidth Bc(G), respectively, and the corresponding decision problem is NP-complete. So estimations of B(G), Bc(G) and in general BH(G) are needed, especially in determining the bandwidths of some specic graphs. In this paper, we rst propose a systematic method for obtaining lower bounds for the bandwidth BH(G). By using this method, we then get a number of lower bounds for B(G) and Bc(G )i n terms of some distance- and degree-related parameters. c 2000 Elsevier Science B.V. All rights reserved.
13349007
Greedy Algorithms for Client Assignment in Large-Scale Distributed Virtual Environments
Distributed Virtual Environments (DVEs), such as online games, military simulations, collaborative design, etc., are very popular nowadays. To support large-scale DVEs, a multi-server architecture is usually employed, and the virtual world is partitioned into multiple zones for load distribution. The client assignment problem arises when assigning the participating clients to the servers. Current approaches usually assign clients to servers according to the locations of clients in the virtual world, i.e., clients interacting in the same zone of the virtual world will be assigned to the same server. This approach may degrade the interactivity of DVEs if the network delay from a client to its assigned server is large. In this paper, we formulate the client assignment problem, and propose two algorithms to assign clients to servers in a more efficient way. The proposed algorithms are based on the heuristics developed for the well-known Terminal Assignment problem. Simulation results with the BRITE Internet Topology Generator show that our algorithms are effective in enhancing the interactivity of DVEs.
963663
Example Based Non-rigid Shape Detection
Since it is hard to handcraft the prior knowledge in a shape detection framework, machine learning methods are preferred to exploit the expert annotation of the target shape in a database. In the previous approaches [1,2] , an optimal similarity transformation is exhaustively searched for to maximize the response of a trained classification model. At best, these approaches only give a rough estimate of the position of a non-rigid shape. In this paper, we propose a novel machine learning based approach to achieve a refined shape detection result. We train a model that has the largest response on a reference shape and a smaller response on other shapes. During shape detection, we search for an optimal non-rigid deformation to maximize the response of the trained model on the deformed image block. Since exhaustive searching is inapplicable for a non-rigid deformation space with a high dimension, currently, example based searching is used instead. Experiments on two applications, left ventricle endocardial border detection and facial feature detection, demonstrate the robustness of our approach. It outperforms the well-known ASM and AAM approaches on challenging samples.
135505
Image based regression using boosting method
We present a general algorithm of image based regression that is applicable to many vision problems. The proposed regressor that targets a multiple-output setting is learned using boosting method. We formulate a multiple-output regression problem in such a way that overfitting is decreased and an analytic solution is admitted. Because we represent the image via a set of highly redundant Haar-like features that can be evaluated very quickly and select relevant features through boosting to absorb the knowledge of the training data, during testing we require no storage of the training data and evaluate the regression function almost in no time. We also propose an efficient training algorithm that breaks the computational bottleneck in the greedy feature selection process. We validate the efficiency of the proposed regressor using three challenging tasks of age estimation, tumor detection, and endocardial wall localization and achieve the best performance with a dramatic speed, e.g., more than 1000 times faster than conventional data-driven techniques such as support vector regressor in the experiment of endocardial wall localization.
17173311
A channel assignment problem for optical networks modelled by Cayley graphs
A problem arising from a recent study of scalability of optical networks seeks to assign channels to the vertices of a network so that vertices distance 2 apart receive distinct channels. In this paper we introduce a general channel assignment scheme for Cayley graphs on abelian groups, and derive upper bounds for the minimum number of channels needed for such graphs. As application we give a systematic way of producing near-optimal channel assignments for connected graphs admitting a vertex-transitive abelian group of automorphisms. Hypercubes are examples of such graphs, and for them our near-optimal upper bound gives rise to the one obtained recently by Wan.