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198
Algorithms for Cluster Busting in Anchored Graph Drawing
 Journal of Graph Algorithms and Applications
, 1998
"... Given a graph G and a drawing or layout of G, it is sometimes desirable to alter or adjust the layout. The challenging aspect of designing layout adjustment algorithms is to maintain a user's mental picture of the original layout. We present a new approach to layout adjustment called cluster bu ..."
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Cited by 37 (0 self)
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busting in anchored graph drawing. We then give two algorithms as examples of this approach. The goals of cluster busting in anchored graph drawing are to more evenly distribute the nodes of the graph in a drawing window while maintaining the user's mental picture of the original drawing. We present
Tradeoffs between Bends and Displacement in Anchored Graph Drawing
"... Many graph drawing applications entail geographical constraints on positions of vertices; these constraints can be at odds with aesthetic requirements such as the use of straightline edges or the number of crossings. Without positional constraints on vertices, of course, every planar graph can be d ..."
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Many graph drawing applications entail geographical constraints on positions of vertices; these constraints can be at odds with aesthetic requirements such as the use of straightline edges or the number of crossings. Without positional constraints on vertices, of course, every planar graph can
Anchor Graph: Global Reordering Contexts for Statistical Machine Translation
"... Reordering poses one of the greatest challenges in Statistical Machine Translation research as the key contextual information may well be beyond the confine of translation units. We present the “Anchor Graph ” (AG) model where we use a graph structure to model global contextual information that is ..."
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Reordering poses one of the greatest challenges in Statistical Machine Translation research as the key contextual information may well be beyond the confine of translation units. We present the “Anchor Graph ” (AG) model where we use a graph structure to model global contextual information
Robust Distributed Network Localization with Noisy Range Measurements
, 2004
"... This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherw ..."
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Cited by 403 (20 self)
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that otherwise corrupt localization computations. We formulate the localization problem as a twodimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable
The PROMPT Suite: Interactive Tools for Ontology Merging and Mapping
 International Journal of HumanComputer Studies
, 2003
"... Researchers in the ontologydesign field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged o ..."
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Cited by 275 (17 self)
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automatic ontology merging: iPrompt is an interactive ontologymerging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. AnchorPrompt uses a graph structure of ontologies to find correlation between concepts
Hashing with graphs
 In ICML
, 2011
"... Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases realworld data lives on a lowdimensional manifold, which should be taken into account t ..."
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Cited by 108 (29 self)
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to capture meaningful nearest neighbors. In this paper, we propose a novel graphbased hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes. To make such an approach computationally feasible, we utilize Anchor Graphs to obtain
AnchorPROMPT: Using NonLocal Context for Semantic Matching
 IN PROCEEDINGS OF THE WORKSHOP ON ONTOLOGIES AND INFORMATION SHARING AT THE INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI
, 2001
"... Researchers in the ontologydesign field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led t ..."
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Cited by 182 (8 self)
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and implemented AnchorPROMPTan algorithm that finds semantically similar terms automatically. AnchorPROMPT takes as input a set of anchorspairs of related terms defined by the user or automatically identified by lexical matching. AnchorPROMPT treats an ontology as a graph with classes as nodes
Drawing bipartite graphs as anchored maps
 In Proc. of APVIS 2006
, 2006
"... A method of drawing anchored maps for bipartite graphs is presented. Suppose that the node set of a bipartite graph is divided into set A and set B. On an anchored map of the bipartite graph, the nodes in A, which are called “anchor nodes, ” are arranged on the circumference, and the nodes in B, whi ..."
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Cited by 10 (2 self)
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A method of drawing anchored maps for bipartite graphs is presented. Suppose that the node set of a bipartite graph is divided into set A and set B. On an anchored map of the bipartite graph, the nodes in A, which are called “anchor nodes, ” are arranged on the circumference, and the nodes in B
Large Graph Construction for Scalable SemiSupervised Learning
"... In this paper, we address the scalability issue plaguing graphbased semisupervised learningviaasmallnumberofanchorpointswhich adequatelycovertheentirepointcloud. Critically, these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted averag ..."
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Cited by 53 (14 self)
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In this paper, we address the scalability issue plaguing graphbased semisupervised learningviaasmallnumberofanchorpointswhich adequatelycovertheentirepointcloud. Critically, these anchor points enable nonparametric regression that predicts the label for each data point as a locally weighted
Anchors in Tournaments
, 1992
"... We define and study the graphtheoretic notion of an anchor in a tournament. An anchor in a tournament is a subset of the nodes of the tournament such that every pair of nodes outside the anchor is `distinguished' at one of the nodes in the anchor. We show that every nnode tournament has an an ..."
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We define and study the graphtheoretic notion of an anchor in a tournament. An anchor in a tournament is a subset of the nodes of the tournament such that every pair of nodes outside the anchor is `distinguished' at one of the nodes in the anchor. We show that every nnode tournament has
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