Results 1  10
of
618
Digraphs  Theory, Algorithms and Applications
, 2007
"... Graph theory is a very popular area of discrete mathematics with not only numerous theoretical developments, but also countless applications to practical problems. As a research area, graph theory is still relatively young, but it is maturing rapidly with many deep results having been discovered o ..."
Abstract

Cited by 231 (50 self)
 Add to MetaCart
Graph theory is a very popular area of discrete mathematics with not only numerous theoretical developments, but also countless applications to practical problems. As a research area, graph theory is still relatively young, but it is maturing rapidly with many deep results having been discovered over the last couple of decades. The theory of graphs can be roughly partitioned into two branches: the areas of undirected graphs and directed graphs (digraphs). Even though both areas have numerous important applications, for various reasons, undirected graphs have been studied much more extensively than directed graphs. One of the reasons is that undirected graphs form in a sense a special class of directed graphs (symmetric digraphs) and hence problems that can be formulated
Complex networks: Structure and dynamics
, 2006
"... Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to ..."
Abstract

Cited by 183 (5 self)
 Add to MetaCart
Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks ’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines,
Capacity of MultiChannel Wireless Networks with Random (c, f) Assignment
, 2007
"... With the availability of multiple unlicensed spectral bands, and potential costbased limitations on the capabilities of individual nodes, it is increasingly relevant to study the performance of multichannel wireless networks with channel switching constraints. To this effect, some constraint models ..."
Abstract

Cited by 160 (9 self)
 Add to MetaCart
With the availability of multiple unlicensed spectral bands, and potential costbased limitations on the capabilities of individual nodes, it is increasingly relevant to study the performance of multichannel wireless networks with channel switching constraints. To this effect, some constraint models have been recently proposed, and connectivity and capacity results have been formulated for networks of randomly deployed singleinterface nodes subject to these constraints. One of these constraint models is termed random (c, f) assignment, wherein each node is preassigned a random subset of f channels out of c (each having bandwidth W c), and may only switch on these. Previous results for this model established bounds on network capacity, and proved that when c = O(logn), the perprnd f flow capacity is O(W nlogn) and Ω(W cnlogn) (where prnd = 1 −(1 − f f f f 2 c)(1 − c−1)...(1 − c − f+1) ≥ 1 − e − c). In this paper we present a lower bound construction that matches the previous upper prnd bound. This establishes the capacity as Θ(W nlogn). The surprising implication of this result is that when f = Ω ( √ c), random (c, f) assignment yields capacity of the same order as attainable via unconstrained switching. The routing/scheduling procedure used by us to achieve capacity requires synchronized routeconstruction for all flows in the network, leading to the open question of whether it is possible to achieve capacity using asynchronous procedures.
Connected Components in Random Graphs with Given Expected Degree Sequences
 ANNALS OF COMBINATORICS
"... ..."
Maximum Entropy Discrimination
, 1999
"... We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections. This holds even when the data is ..."
Abstract

Cited by 122 (20 self)
 Add to MetaCart
We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections. This holds even when the data is not separable within the chosen parametric class, in the context of anomaly detection rather than classification, or when the labels in the training set are uncertain or incomplete. Support vector machines are naturally subsumed under this class and we provide several extensions. We are also able to estimate exactly and efficiently discriminative distributions over tree structures of classconditional models within this framework. Preliminary experimental results are indicative of the potential in these techniques.
Learning with mixtures of trees
 Journal of Machine Learning Research
, 2000
"... This paper describes the mixturesoftrees model, a probabilistic model for discrete multidimensional domains. Mixturesoftrees generalize the probabilistic trees of Chow and Liu [6] in a different and complementary direction to that of Bayesian networks. We present efficient algorithms for learnin ..."
Abstract

Cited by 109 (2 self)
 Add to MetaCart
This paper describes the mixturesoftrees model, a probabilistic model for discrete multidimensional domains. Mixturesoftrees generalize the probabilistic trees of Chow and Liu [6] in a different and complementary direction to that of Bayesian networks. We present efficient algorithms for learning mixturesoftrees models in maximum likelihood and Bayesian frameworks. We also discuss additional efficiencies that can be obtained when data are “sparse, ” and we present data structures and algorithms that exploit such sparseness. Experimental results demonstrate the performance of the model for both density estimation and classification. We also discuss the sense in which treebased classifiers perform an implicit form of feature selection, and demonstrate a resulting insensitivity to irrelevant attributes.
Algorithmic Aspects of Topology Control Problems for Ad hoc Networks
, 2002
"... Topology control problems are concerned with the assignment of power values to the nodes of an ad~hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing th ..."
Abstract

Cited by 101 (6 self)
 Add to MetaCart
Topology control problems are concerned with the assignment of power values to the nodes of an ad~hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties called monotone properties. The difficulty of generalizing the approach to properties that are not monotone is discussed. Problems involving the minimization of total power are known to be NPcomplete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2nodeconnected graph is obtained. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.
Characterization of complex networks: A survey of measurements
 Advances in Physics
"... Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of mea ..."
Abstract

Cited by 88 (7 self)
 Add to MetaCart
Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics and function of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements organized into classes. Special attention is given to relating complex network analysis with the areas of pattern recognition and feature selection, as well as on surveying some concepts and measurements from traditional graph theory which are potentially useful for complex network research. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the
Distributed compressed sensing
, 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
Abstract

Cited by 84 (21 self)
 Add to MetaCart
Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multisignal ensembles that exploit both intra and intersignal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. We study in detail three simple models for jointly sparse signals, propose algorithms for joint recovery of multiple signals from incoherent projections, and characterize theoretically and empirically the number of measurements per sensor required for accurate reconstruction. We establish a parallel with the SlepianWolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In two of our three models, the results are asymptotically bestpossible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. In some sense DCS is a framework for distributed compression of sources with memory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.