A randomized parallel algorithm for single-source shortest paths
Journal of Algorithms
Topology discovery for large ethernet networks
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
A Parallelization of Dijkstra's Shortest Path Algorithm
MFCS '98 Proceedings of the 23rd International Symposium on Mathematical Foundations of Computer Science
Reachability and Distance Queries via 2-Hop Labels
SIAM Journal on Computing
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Computing the shortest path: A search meets graph theory
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Taming verification hardness: an efficient algorithm for testing subgraph isomorphism
Proceedings of the VLDB Endowment
On-line exact shortest distance query processing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
GAIA: graph classification using evolutionary computation
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Connected substructure similarity search
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Neighborhood-privacy protected shortest distance computing in cloud
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Neighborhood based fast graph search in large networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Distance-constraint reachability computation in uncertain graphs
Proceedings of the VLDB Endowment
LTS: Discriminative subgraph mining by learning from search history
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
On dimensionality reduction of massive graphs for indexing and retrieval
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Fast graph query processing with a low-cost index
The VLDB Journal — The International Journal on Very Large Data Bases
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Many recent large-scale data intensive applications are increasingly demanding efficient graph databases. Distributed graph algorithms, as a core part of practical graph databases, have a wide range of important applications, but have been rarely studied in sufficient detail. These problems are challenging as real graphs are usually extremely large and the intrinsic character of graph data, lacking locality, causes unbalanced computation and communication workloads. In this paper, we explore distributed breadth-first search algorithms with regards to large-scale applications. We propose DPC (Degree-based Partitioning and Communication), a scalable and efficient distributed BFS algorithm which achieves high scalability and performance through novel balancing techniques between computation and communication. In experimental study, we compare our algorithm with two state-of-the-art algorithms under the Graph500 benchmark with a variety of settings. The result shows our algorithm significantly outperforms the existing algorithms under all the settings.