A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
The budgeted maximum coverage problem
Information Processing Letters
Communications of the ACM
IDMaps: a global internet host distance estimation service
IEEE/ACM Transactions on Networking (TON)
A road network embedding technique for k-nearest neighbor search in moving object databases
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Triangulation and Embedding Using Small Sets of Beacons
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Journal of the ACM (JACM)
Computing the shortest path: A search meets graph theory
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Using structure indices for efficient approximation of network properties
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance indexing on road networks
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Voronoi-based K nearest neighbor search for spatial network databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Hierarchical Graph Embedding for Efficient Query Processing in Very Large Traffic Networks
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Fast shortest path distance estimation in large networks
Proceedings of the 18th ACM conference on Information and knowledge management
A sketch-based distance oracle for web-scale graphs
Proceedings of the third ACM international conference on Web search and data mining
TEDI: efficient shortest path query answering on graphs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Searching connected API subgraph via text phrases
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Shortest-path queries in static networks
ACM Computing Surveys (CSUR)
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Shortest paths and shortest path distances are important primary queries for users to query in a large graph. In this paper, we propose a new approach to answer shortest path and shortest path distance queries efficiently with an error bound. The error bound is controlled by a user-specified parameter, and the online query efficiency is achieved with prepossessing offline. In the offline preprocessing, we take a reference node embedding approach which computes the single-source shortest paths from each reference node to all the other nodes. To guarantee the user-specified error bound, we design a novel coverage-based reference node selection strategy, and show that selecting the optimal set of reference nodes is NP-hard. We propose a greedy selection algorithm which exploits the submodular property of the formulated objective function, and use a graph partitioning-based heuristic to further reduce the offline computational complexity of reference node embedding. In the online query answering, we use the precomputed distances to provide a lower bound and an upper bound of the true shortest path distance based on the triangle inequality. In addition, we propose a linear algorithm which computes the approximate shortest path between two nodes within the error bound. We perform extensive experimental evaluation on a large-scale road network and a social network and demonstrate the effectiveness and efficiency of our proposed methods.