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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
On graph problems in a semistreaming model �
"... www.elsevier.com/locate/tcs 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 ..."
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www.elsevier.com/locate/tcs 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
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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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
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
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
SYNONYMS Graph Streams; SemiStreaming Model Graph Mining on Streams
"... Consider a data stream A = 〈a1, a2,..., am 〉 where each data item ak ∈ [n] × [n]. Such a stream naturally defines an undirected, unweighted graph G = (V, E) where V = {v1,..., vn} and, E = {(vi, vj) : ak = (i, j) for some k ∈ [m]}. Graph mining on streams is concerned with estimating properties of ..."
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to be simulated [2]. The StreamSort model goes one step further and allows sorting passes in which the data stream is sorted according to a key encoded by the annotations [1]. Weighted, Dynamic, or Directed Graphs: For many problems it is implicitly assumed that the elements ak are distinct. When the data items
A Framework for Dynamic Graph Drawing
 CONGRESSUS NUMERANTIUM
, 1992
"... Drawing graphs is an important problem that combines flavors of computational geometry and graph theory. Applications can be found in a variety of areas including circuit layout, network management, software engineering, and graphics. The main contributions of this paper can be summarized as follows ..."
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Cited by 627 (44 self)
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Drawing graphs is an important problem that combines flavors of computational geometry and graph theory. Applications can be found in a variety of areas including circuit layout, network management, software engineering, and graphics. The main contributions of this paper can be summarized
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