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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 532 (48 self)
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, and we observe some surprising phenomena. First, most of these graphs densify over time, with the number of edges growing superlinearly in the number of nodes. Second, the average distance between nodes often shrinks over time, in contrast to the conventional wisdom that such distance parameters should
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (52 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Property Testing and its connection to Learning and Approximation
"... We study the question of determining whether an unknown function has a particular property or is fflfar from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the fun ..."
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Cited by 475 (67 self)
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w.r.t the vertex set). Our graph property testing algorithms are probabilistic and make assertions which are correct with high probability, utilizing only poly(1=ffl) edgequeries into the graph, where ffl is the distance parameter. Moreover, the property testing algorithms can be used
The geometry of graphs and some of its algorithmic applications
 COMBINATORICA
, 1995
"... In this paper we explore some implications of viewing graphs as geometric objects. This approach offers a new perspective on a number of graphtheoretic and algorithmic problems. There are several ways to model graphs geometrically and our main concern here is with geometric representations that res ..."
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Cited by 521 (19 self)
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that respect the metric of the (possibly weighted) graph. Given a graph G we map its vertices to a normed space in an attempt to (i) Keep down the dimension of the host space and (ii) Guarantee a small distortion, i.e., make sure that distances between vertices in G closely match the distances between
Similarity of Color Images
, 1995
"... We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 , L 2 , or L1 distance between two cumulative color histograms can be used to define a similarity mea ..."
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Cited by 490 (2 self)
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We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1 , L 2 , or L1 distance between two cumulative color histograms can be used to define a similarity
EXPANSION IN THE DISTANCE PARAMETER FOR TWO VORTICES CLOSE TOGETHER
, 2000
"... Static vortices close together are studied for two different models in 2dimensional Euclidean space. In a simple model for one complex field an expansion in the parameters describing the relative position of two vortices can be given in terms of trigonometric and exponential functions. The results ..."
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Static vortices close together are studied for two different models in 2dimensional Euclidean space. In a simple model for one complex field an expansion in the parameters describing the relative position of two vortices can be given in terms of trigonometric and exponential functions. The results
Generating the Vertex Sets with some Distance Parameter Properties in Caterpillar Graphs
"... In this paper the vertices of caterpillar tree are viewed with different approach and categorized into the sets, and, based on the distance parameters i.e., diameter and radius. The distance parameters have been presented with some set theory views. Here is the set of diametral vertices, is the set ..."
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In this paper the vertices of caterpillar tree are viewed with different approach and categorized into the sets, and, based on the distance parameters i.e., diameter and radius. The distance parameters have been presented with some set theory views. Here is the set of diametral vertices, is the set
Insertion sequences
 Microbiol Mol. Biol. Rev
, 1998
"... These include: Receive: RSS Feeds, eTOCs, free email alerts (when new articles cite this article), more» Downloaded from ..."
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Cited by 426 (3 self)
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These include: Receive: RSS Feeds, eTOCs, free email alerts (when new articles cite this article), more» Downloaded from
Tracking multiple independent targets: Evidence for a parallel tracking mechanism
 Spatial Vision
, 1988
"... AbstractThere is considerable evidence that visual attention is concentrated at a single locus in the visual field, and that this locus can be moved independent of eye movements. Two studies are reported which suggest that, while certain aspects of attention require that locations\be scanned serial ..."
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Cited by 361 (23 self)
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;identical randomlymoving objects in order to distinguish a change in a target from a change in a distractor; and (b) when the speed and distance parameters of the display are designed so that, on the basis of some very conservative assumptions about the speed of attention movement and encoding times, the predicted
THERMODYNAMICS OF ADSORPTION AND GIBBSIAN DISTANCE PARAMETERS IN TWO AND THREEPHASE SYSTEMS
"... Abstract—In a multicomponent, 2phase system, the Gibbs dividing surfaces for the respective components are separated by characteristic distances, A, = 17/iXc5. (I) More generally, if b designates an arbitrary criterion defining a surface, e.g. F = F, and d designates another criterion, e.g. the ..."
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Abstract—In a multicomponent, 2phase system, the Gibbs dividing surfaces for the respective components are separated by characteristic distances, A, = 17/iXc5. (I) More generally, if b designates an arbitrary criterion defining a surface, e.g. F = F, and d designates another criterion, e
Results 1  10
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1,360,484