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Fast and Accurate Estimation of Shortest Paths in Large Networks

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44541

Gubichev,  Andrey
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44104

Bedathur,  Srikanta
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45462

Seufert,  Stephan
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Gubichev, A., Bedathur, S., Seufert, S., & Weikum, G. (2010). Fast and Accurate Estimation of Shortest Paths in Large Networks. In X. J. Huang, G. Jones, N. Koudas, X. Wu, & K. Collins-Thompson (Eds.), Proceedings of the 19th ACM Conference on Information and Knowledge Management (pp. 499-508). New York, NY: ACM. doi:10.1145/1871437.1871503.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-14EF-6
Abstract
Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large disk-resident instances of graph data. While a number of techniques exist for answering reachability queries and approximating node distances efficiently, determining actual shortest paths (i. e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes. In this paper, we address this problem and present a scalable sketch-based index structure that not only supports estimation of node distances, but also computes corresponding shortest paths themselves. Generating the actual path information allows for further improvements to the estimation accuracy of distances (and paths), leading to near-exact shortest-path approximations in real world graphs. We evaluate our techniques – implemented within a fully functional RDF graph database system – over large real-world social and biological networks of sizes ranging from tens of thousand to millions of nodes and edges. Experiments on several datasets show that we can achieve query response times providing several orders of magnitude speedup over traditional path computations while keeping the estimation errors between 0% and 1% on average.