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1,305
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 t ..."
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Cited by 428 (9 self)
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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,
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 ..."
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Cited by 382 (54 self)
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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
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 ..."
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Cited by 269 (11 self)
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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
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Capacity of Wireless Erasure Networks
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring eac ..."
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Cited by 150 (12 self)
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In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring each node to send the same signal on all outgoing channels. However, we assume there is no interference in reception. Such models are therefore appropriate for wireless networks where all information transmission is packetized and where some mechanism for interference avoidance is already built in. This paper looks at multicast problems over these networks. The capacity under the assumption that erasure locations on all the links of the network are provided to the destinations is obtained. It turns out that the capacity region has a nice maxflow mincut interpretation. The definition of cutcapacity in these networks incorporates the broadcast property of the wireless medium. It is further shown that linear coding at nodes in the network suffices to achieve the capacity region. Finally, the performance of different coding schemes in these networks when no side information is available to the destinations is analyzed.
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 ..."
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Cited by 141 (21 self)
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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 ..."
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Cited by 141 (2 self)
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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.
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 ..."
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Cited by 137 (25 self)
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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.
Barrier coverage with wireless sensors
 In ACM MobiCom
, 2005
"... When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every ..."
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Cited by 133 (9 self)
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When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least £ distinct sensors before it crosses the barrier of wireless sensors, we say the network provides £barrier coverage. In this paper, we develop theoretical foundations for £barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is £barrier covered. Next, we establish the optimal deployment pattern to achieve £barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for £barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak £barrier coverage.