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A Random Graph Model for Massive Graphs
 STOC 2000
, 2000
"... We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from t ..."
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Cited by 414 (26 self)
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We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from
Random Evolution in Massive Graphs
, 2001
"... Many massive graphs (such as WWW graphs and Call graphs) share certain universal characteristics which can be described by socalled the "power law". In this paper, we will first briefly survey the history and previous work on power law graphs. Then we will give four evolution models for ge ..."
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Cited by 105 (7 self)
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Many massive graphs (such as WWW graphs and Call graphs) share certain universal characteristics which can be described by socalled the "power law". In this paper, we will first briefly survey the history and previous work on power law graphs. Then we will give four evolution models
The Diameter of Random Massive Graphs
 Proceedings of the Twelfth ACMSIAM Symposium on Discrete Algorithms
, 2000
"... Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by socalled the "power law". Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show ..."
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Cited by 36 (10 self)
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Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by socalled the "power law". Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results
Managing Massive Graphs
, 2009
"... Abstract. Many real graphs conform today some of the largest data sets. Some of the best representatives of these graphs are the web graph, the interconnection network graph, the telephone callgraph, social networks, and query log graphs. Managing and finding relevant information on large graphs ar ..."
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are challenging problems in current research. The need to deal with massive graphs has increased the interest in different research areas, such as compact data structures, data streaming, graph mining, secondary storage, and distributed algorithms. In this thesis, we propose to study the management of massive
Massive graph triangulation
 In ACM SIGMOD Conference on Management of Data
, 2013
"... This paper studies I/Oefficient algorithms for settling the classic triangle listing problem, whose solution is a basic operator in dealing with many other graph problems. Specifically, given an undirected graph G, the objective of triangle listing is to find all the cliques involving 3 vertices ..."
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Cited by 12 (0 self)
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This paper studies I/Oefficient algorithms for settling the classic triangle listing problem, whose solution is a basic operator in dealing with many other graph problems. Specifically, given an undirected graph G, the objective of triangle listing is to find all the cliques involving 3 vertices
Abstract The Diameter of Random Massive Graphs ∗
"... Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by socalled the “power law”. Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show a strong ..."
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Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by socalled the “power law”. Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show a strong
Property Testing in Massive Graphs
, 1999
"... 0 1 Introduction Suppose we are given a huge graph representing some binary relation over a huge dataset (see below), and we need to determine whether the graph (equiv., the relation) has some predetermined property. Since the graph is huge, we cannot or do not want to even scan all of it (let alon ..."
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0 1 Introduction Suppose we are given a huge graph representing some binary relation over a huge dataset (see below), and we need to determine whether the graph (equiv., the relation) has some predetermined property. Since the graph is huge, we cannot or do not want to even scan all of it (let
Parallel Community Detection for Massive Graphs
"... Abstract. Tackling the current volume of graphstructured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices and ne ..."
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Cited by 15 (4 self)
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Abstract. Tackling the current volume of graphstructured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to graphs of over 122 million vertices
COUNTING TRIANGLES IN MASSIVE GRAPHS WITH MAPREDUCE
, 2013
"... Graphs and networks are used to model interactions in a variety of contexts. There is a growing need to quickly assess the characteristics of a graph in order to understand its underlying structure. Some of the most useful metrics are trianglebased and give a measure of the connectedness of mutual ..."
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Cited by 12 (4 self)
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algorithm has proved successful in efficiently and accurately estimating clustering coefficients. In this paper, we describe how to implement this approach in MapReduce to deal with extremely massive graphs. We show results on publiclyavailable networks, the largest of which is 132M nodes and 4.7B edges
ABSTRACT A Random Graph Model for Massive Graphs
"... We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from the ..."
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We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from
Results 1  10
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129,158