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On total traffic domination in noncomplete graphs
"... Given a graph G(V,E), a set of traffic matrices H and one additional traffic matrix h, we say that H totally dominates h if for each capacity reservation u supporting H, u also supports h using the same routing pattern. It has been shown that if H  = 1 and G is a complete graph, H totally dominat ..."
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Given a graph G(V,E), a set of traffic matrices H and one additional traffic matrix h, we say that H totally dominates h if for each capacity reservation u supporting H, u also supports h using the same routing pattern. It has been shown that if H  = 1 and G is a complete graph, H totally
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
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Cited by 801 (1 self)
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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
Analysis, Modeling and Generation of SelfSimilar VBR Video Traffic
, 1994
"... We present a detailed statistical analysis of a 2hour long empirical sample of VBR video. The sample was obtained by applying a simple intraframe video compression code to an action movie. The main findings of our analysis are (1) the tail behavior of the marginal bandwidth distribution can be accu ..."
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Cited by 546 (6 self)
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be accurately described using "heavytailed" distributions (e.g., Pareto); (2) the autocorrelation of the VBR video sequence decays hyperbolically (equivalent to longrange dependence) and can be modeled using selfsimilar processes. We combine our findings in a new (nonMarkovian) source model
Widearea Internet traffic patterns and characteristics
 IEEE Network
, 1997
"... Abstract – The Internet is rapidly growing in number of users, traffic levels, and topological complexity. At the same time it is increasingly driven by economic competition. These developments render the characterization of network usage and workloads more difficult, and yet more critical. Few rece ..."
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Cited by 521 (0 self)
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Abstract – The Internet is rapidly growing in number of users, traffic levels, and topological complexity. At the same time it is increasingly driven by economic competition. These developments render the characterization of network usage and workloads more difficult, and yet more critical. Few
Secure Group Communications Using Key Graphs
, 1998
"... Many emerging applications (e.g., teleconference, realtime information services, pay per view, distributed interactive simulation, and collaborative work) are based upon a group communications model, i.e., they require packet delivery from one or more authorized senders to a very large number of au ..."
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Cited by 552 (17 self)
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management. We formalize the notion of a secure group as a triple (U; K;R) where U denotes a set of users, K a set of keys held by the users, and R a userkey relation. We then introduce key graphs to specify secure groups. For a special class of key graphs, we present three strategies for securely
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Interprocedural Slicing Using Dependence Graphs
 ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS
, 1990
"... ... This paper concerns the problem of interprocedural slicinggenerating a slice of an entire program, where the slice crosses the boundaries of procedure calls. To solve this problem, we introduce a new kind of graph to represent programs, called a system dependence graph, which extends previou ..."
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Cited by 822 (85 self)
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... This paper concerns the problem of interprocedural slicinggenerating a slice of an entire program, where the slice crosses the boundaries of procedure calls. To solve this problem, we introduce a new kind of graph to represent programs, called a system dependence graph, which extends
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
, 2005
"... How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include hea ..."
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Cited by 534 (48 self)
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How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include
Fast Planning Through Planning Graph Analysis
 ARTIFICIAL INTELLIGENCE
, 1995
"... We introduce a new approach to planning in STRIPSlike domains based on constructing and analyzing a compact structure we call a Planning Graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest possible partialorder plan, or states that no valid pla ..."
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Cited by 1165 (3 self)
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We introduce a new approach to planning in STRIPSlike domains based on constructing and analyzing a compact structure we call a Planning Graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest possible partialorder plan, or states that no valid
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