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1,357
Frequent Subgraph Discovery
, 2001
"... Over the years, frequent itemset discovery algorithms have been used to solve various interesting problems. As data mining techniques are being increasingly applied to nontraditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of th ..."
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Cited by 406 (10 self)
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computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input
Dryad: Distributed DataParallel Programs from Sequential Building Blocks
 In EuroSys
, 2007
"... Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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Cited by 762 (27 self)
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Dryad is a generalpurpose distributed execution engine for coarsegrain dataparallel applications. A Dryad application combines computational “vertices ” with communication “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set
An algorithm for subgraph isomorphism
 JOURNAL OF THE ACM
, 1976
"... Subgraph isomorphism can be determined by means of a bruteforce treesearch enumeration procedure. In this paper a new algorithm is introduced that attains efficiency by inferentially eliminating successor nodes in the tree search. To assess the time actually taken by the new algorithm, subgraph is ..."
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Cited by 347 (1 self)
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isomorphism, clique detection, graph isomorphism, and directed graph isomorphism experiments have been carried out with random and with various nonrandom graphs. A parallel asynchronous logicinmemory implementation of a vital part of the algorithm is also described, although this hardware has not actually
Scalable molecular dynamics with NAMD.
 J Comput Chem
, 2005
"... Abstract: NAMD is a parallel molecular dynamics code designed for highperformance simulation of large biomolecular systems. NAMD scales to hundreds of processors on highend parallel platforms, as well as tens of processors on lowcost commodity clusters, and also runs on individual desktop and la ..."
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Cited by 849 (63 self)
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Abstract: NAMD is a parallel molecular dynamics code designed for highperformance simulation of large biomolecular systems. NAMD scales to hundreds of processors on highend parallel platforms, as well as tens of processors on lowcost commodity clusters, and also runs on individual desktop
Cumulated Gainbased Evaluation of IR Techniques
 ACM Transactions on Information Systems
, 2002
"... Modem large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to the users. In order to develop IR techniques to this direction, i ..."
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Cited by 694 (3 self)
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along the ranked result list. The second one is similar but applies a discount factor on the relevance scores in order to devaluate lateretrieved documents. The third one computes the relativetothe ideal performance of IR techniques, based on the cumulative gain they are able to yield. The novel
Partitioning of Unstructured Problems for Parallel Processing
, 1991
"... Many large scale computational problems are based on unstructured computational domains. Primary examples are unstructured grid calculations based on finite volume methods in computational fluid dynamics, or structural analysis problems based on finite element approximations. Here we will address th ..."
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Cited by 344 (16 self)
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a new decomposition algorithm will be discussed, which is based on the computation of an eigenvector of the Laplacian matrix associated with the graph. Numerical comparisons on large scale two and three dimensional problems demonstrate the superiority of the new spectral bisection algorithm.
Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors
, 1999
"... Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported ..."
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Cited by 326 (5 self)
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Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported
Fast Parallel Conversion of Edge List to Adjacency List for LargeScale Graphs
"... In the era of bigdata, we are deluged with massive graph data emerged from numerous social and scientific applications. In most cases, graph data are generated as lists of edges (edge list), where an edge denotes a link between a pair of entities. However, most of the graph algorithms work efficien ..."
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efficiently when information of the adjacent nodes (adjacency list) for each node are readily available. Although the conversion from edge list to adjacency list can be trivially done on the fly for small graphs, such conversion becomes challenging for the emerging largescale graphs consisting billions
DryadLINQ: A System for GeneralPurpose Distributed DataParallel Computing Using a HighLevel Language
"... DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale distributed computing. It generalizes previous execution environments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of strongly typed.NET objects; and by s ..."
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Cited by 273 (27 self)
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made up of thousands of computers, ensures efficient, reliable execution of this plan. We describe the implementation of the DryadLINQ compiler and runtime. We evaluate DryadLINQ on a varied set of programs drawn from domains such as webgraph analysis, largescale log mining, and machine learning. We
GraphChi: Largescale Graph Computation On just a PC
 In Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation, OSDI’12
, 2012
"... Current systems for graph computation require a distributed computing cluster to handle very large realworld problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains c ..."
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Cited by 115 (6 self)
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, GraphChi can solve the same problems in very reasonable time. Our work makes largescale graph computation available to anyone with a modern PC. 1
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