Proceedings of the 19th international conference on World wide web
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Coniunge et impera: multiple-graph mining for query-log analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
A middleware for parallel processing of large graphs
Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science
Integrating MapReduce and RDBMSs
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
An efficient distributed subgraph mining algorithm in extreme large graphs
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
CPRS: A cloud-based program recommendation system for digital TV platforms
Future Generation Computer Systems
Social content matching in MapReduce
Proceedings of the VLDB Endowment
Fast personalized PageRank on MapReduce
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
A unified representation of web logs for mining applications
Information Retrieval
An implementation framework of mapreduce email social network analysis
Proceedings of the 6th ACM workshop on Wireless multimedia networking and computing
The Combinatorial BLAS: design, implementation, and applications
International Journal of High Performance Computing Applications
ParallelGDB: a parallel graph database based on cache specialization
Proceedings of the 15th Symposium on International Database Engineering & Applications
CPRS: a cloud-based program recommendation system for digital TV platforms
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Distributed graph pattern matching
Proceedings of the 21st international conference on World Wide Web
Managing and mining large graphs: systems and implementations
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Integrating open government data with stratosphere for more transparency
Web Semantics: Science, Services and Agents on the World Wide Web
MapReduce in MPI for Large-scale graph algorithms
Parallel Computing
Adapting scientific computing problems to clouds using MapReduce
Future Generation Computer Systems
Truss decomposition in massive networks
Proceedings of the VLDB Endowment
Vertex neighborhoods, low conductance cuts, and good seeds for local community methods
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A MapReduce-supported network structure for data centers
Concurrency and Computation: Practice & Experience
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Triangle listing in massive networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Multimedia Applications and Security in MapReduce: Opportunities and Challenges
Concurrency and Computation: Practice & Experience
Degree relations of triangles in real-world networks and graph models
Proceedings of the 21st ACM international conference on Information and knowledge management
CC-MR --- finding connected components in huge graphs with mapreduce
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Mining dense structures to uncover anomalous behaviour in financial network data
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Inexact subgraph isomorphism in MapReduce
Journal of Parallel and Distributed Computing
Computational Engineering in the Cloud: Benefits and Challenges
Journal of Organizational and End User Computing
Exploiting and Evaluating MapReduce for Large-Scale Graph Mining
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Using Pregel-like Large Scale Graph Processing Frameworks for Social Network Analysis
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A space efficient streaming algorithm for triangle counting using the birthday paradox
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2nd ACM SIGPLAN workshop on Functional high-performance computing
An efficient MapReduce algorithm for counting triangles in a very large graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Parallel triangle counting in massive streaming graphs
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Analysis of partitioning strategies for graph processing in bulk synchronous parallel models
Proceedings of the fifth international workshop on Cloud data management
Bisimulation reduction of big graphs on mapreduce
BNCOD'13 Proceedings of the 29th British National conference on Big Data
Why do simple algorithms for triangle enumeration work in the real world?
Proceedings of the 5th conference on Innovations in theoretical computer science
Proceedings of the VLDB Endowment
Parallel processing of large graphs
Future Generation Computer Systems
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As the size of graphs for analysis continues to grow, methods of graph processing that scale well have become increasingly important. One way to handle large datasets is to disperse them across an array of networked computers, each of which implements simple sorting and accumulating, or MapReduce, operations. This cloud computing approach offers many attractive features. If decomposing useful graph operations in terms of MapReduce cycles is possible, it provides incentive for seriously considering cloud computing. Moreover, it offers a way to handle a large graph on a single machine that can't hold the entire graph as well as enables streaming graph processing. This article examines this possibility.