On the dynamic shortest path problem
Journal of Information Processing
Incremental algorithms for minimal length paths
Journal of Algorithms
An incremental algorithm for a generalization of the shortest-path problem
Journal of Algorithms
Faster shortest-path algorithms for planar graphs
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Sparsification—a technique for speeding up dynamic graph algorithms
Journal of the ACM (JACM)
A new approach to dynamic all pairs shortest paths
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Fully Dynamic Algorithms for Maintaining All-Pairs Shortest Paths and Transitive Closure in Digraphs
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Fully Dynamic All Pairs Shortest Paths with Real Edge Weights
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
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Today's social networks are getting larger, and the need to analyze datasets with millions of nodes and billions of edges is not uncommon any more. As a network of social relationships evolves by the addition of new nodes and edges, fast algorithms are desirable for the recomputation of key network measures such as actor centrality. The distributed computing paradigm offers a scalable approach to addressing the recomputation challenge. This paper develops a Map-Reduce implementation of an incremental All-Pairs Shortest Path (APSP) algorithm. The incremental nature of the approach allows for performing minimal work in updating centrality measures, while the Map-Reduce implementation makes it scalable to large data. The key idea of the incremental APSP algorithm [1] is based on the efficient use of past information about the shortest paths between any node and the neighbors of the newly added node. A presented parallelized version of the algorithm relies on a three-step iterative execution of the "map" and "reduce" jobs. Experiences with its implementation are reported in application to a real-world dataset containing 7115 nodes. The experimental runs were performed using the Amazon's EMR service.