Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A New Parallel and Distributed Shortest Path Algorithm for Hierarchically Clustered Data Networks
IEEE Transactions on Parallel and Distributed Systems
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Buckets, heaps, lists, and monotone priority queues
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Journal of the ACM (JACM)
Efficient Algorithms for Shortest Paths in Sparse Networks
Journal of the ACM (JACM)
Communications of the ACM
Maintaining Minimum Spanning Forests in Dynamic Graphs
SIAM Journal on Computing
FATES: Finding A Time dEpendent Shortest path
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Dynamic Shortest Paths Minimizing Travel Times and Costs
Dynamic Shortest Paths Minimizing Travel Times and Costs
A dynamic multicast routing satisfying multiple QoS constraints
International Journal of Network Management
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering Structural Anomalies in Graph-Based Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Finding time-dependent shortest paths over large graphs
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Web graph similarity for anomaly detection (poster)
Proceedings of the 17th international conference on World Wide Web
Estimating the size of the transitive closure in linear time
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Social Network Data Analytics
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Networks can be represented as evolutionary graphs in a variety of spatio-temporal applications. Changes in the nodes and edges over time may also result in corresponding changes in structural garph properties such as shortest path distances. In this paper, we study the problem of detecting the top-k most significant shortest-path distance changes between two snapshots of an evolving graph. While the problem is solvable with two applications of the all-pairs shortest path algorithm, such a solution would be extremely slow and impractical for very large graphs. This is because when a graph may contain millions of nodes, even the storage of distances between all node pairs can become inefficient in practice. Therefore, it is desirable to design algorithms which can directly determine the significant changes in shortest path distances, without materializing the distances in individual snapshots. We present algorithms that are up to two orders of magnitude faster than such a solution, while retaining comparable accuracy.