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Graph Sparsification in the Semistreaming Model
, 2009
"... Analyzing massive data sets has been one of the key motivations for studying streaming algorithms. In recent years, there has been significant progress in analysing distributions in a streaming setting, but the progress on graph problems has been limited. A main reason for this has been the existenc ..."
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Cited by 21 (5 self)
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of the semistreaming model where we assume that the space is (near) linear in the number of vertices (but not necessarily the edges), and the edges appear in an arbitrary (and possibly adversarial) order. However there has been limited progress in analysing graph algorithms in this model. In this paper we focus
SPECTRAL SPARSIFICATION IN THE SEMISTREAMING SETTING
"... Abstract. Let G be a graph with n vertices and m edges. A sparsifier of G is a sparse graph on the same vertex set approximating G in some natural way. It allows us to say useful things about G while considering much fewer than m edges. The strongest commonlyused notion of sparsification is spectra ..."
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Cited by 18 (1 self)
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is spectral sparsification; H is a spectral sparsifier of G if the quadratic forms induced by the Laplacians of G and H approximate one another well. This notion is strictly stronger than the earlier concept of combinatorial sparsification. In this paper, we consider a semistreaming setting, where we have
Sparsification Algorithm for Cut Problems on Semistreaming Model
, 2009
"... The emergence of social networks and other interaction networks have brought to fore the questions of processing massive graphs. The (semi) streaming model, where we assume that the space is (near) linear in the number of vertices (but not necessarily the edges) is an useful and efficient model for ..."
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for processing large graphs. In many of these graphs the numbers of vertices are significantly less than the number of edges, and hence attract the semistreaming model. We focus on the problem of graph sparsification in a single pass, that is, constructing a small space representation of the graph such that we
kconnectivity in the semistreaming model
, 2006
"... We present the first semistreaming algorithms to determine kconnectivity of an undirected graph with k being any constant. The semistreaming model for graph algorithms was introduced by Muthukrishnan in 2003 and turns out to be useful when dealing with massive graphs streamed in from an external ..."
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Cited by 4 (0 self)
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We present the first semistreaming algorithms to determine kconnectivity of an undirected graph with k being any constant. The semistreaming model for graph algorithms was introduced by Muthukrishnan in 2003 and turns out to be useful when dealing with massive graphs streamed in from an external
Analyzing Massive Graphs in the Semistreaming Model
"... Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, large scale scientific experiments, and clustering of large data sets. The semistreaming model was proposed for processing massive graphs. In the semistreaming model, we have a random accessible memory ..."
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Cited by 1 (0 self)
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Massive graphs arise in a many scenarios, for example, traffic data analysis in large networks, large scale scientific experiments, and clustering of large data sets. The semistreaming model was proposed for processing massive graphs. In the semistreaming model, we have a random accessible memory
On graph problems in a semistreaming model
 In 31st International Colloquium on Automata, Languages and Programming
, 2004
"... Abstract. We formalize a potentially rich new streaming model, the semistreaming model, that we believe is necessary for the fruitful study of efficient algorithms for solving problems on massive graphs whose edge sets cannot be stored in memory. In this model, the input graph, G = (V, E), is prese ..."
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Cited by 108 (16 self)
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Abstract. We formalize a potentially rich new streaming model, the semistreaming model, that we believe is necessary for the fruitful study of efficient algorithms for solving problems on massive graphs whose edge sets cannot be stored in memory. In this model, the input graph, G = (V, E
SemiStreaming Set Cover (Full Version)
"... This paper studies the set cover problem under the semistreaming model. The underlying set system is formalized in terms of a hypergraph G = (V,E) whose edges arrive onebyone and the goal is to construct an edge cover F ⊆ E with the objective of minimizing the cardinality (or cost in the weighted ..."
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This paper studies the set cover problem under the semistreaming model. The underlying set system is formalized in terms of a hypergraph G = (V,E) whose edges arrive onebyone and the goal is to construct an edge cover F ⊆ E with the objective of minimizing the cardinality (or cost
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
WEIGHTED MATCHING IN THE SEMISTREAMING MODEL
, 2008
"... We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukrishnan in ..."
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Cited by 13 (1 self)
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We reduce the best known approximation ratio for finding a weighted matching of a graph using a onepass semistreaming algorithm from 5.828 to 5.585. The semistreaming model forbids random access to the input and restricts the memory to O(n · polylog n) bits. It was introduced by Muthukrishnan
Object exchange across heterogeneous information sources
 INTERNATIONAL CONFERENCE ON DATA ENGINEERING
, 1995
"... We address the problem of providing integrated access to diverse and dynamic information sources. We explain how this problem differs from the traditional database integration problem and we focus on one aspect of the information integration problem, namely information exchange. We define an object ..."
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Cited by 513 (57 self)
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based information exchange model and a corresponding query language that we believe are well suited for integration of diverse information sources. We describe how, the model and language have been used to integrate heterogeneous bibliographic information sources. We also describe two generalpurpose libraries we
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