Abstract
Shortest Path Query is being applied to more and more professional scopes, such as social networks and bioinformatics. But the exponential growth of data makes it much more challenging since traditional BFS-based algorithms are hard to scale due to the requirement of huge memory.
Different from the traditional algorithm such as Dijkstra algorithm, our method is based on Depth-First-Search, which first constructs the DFS tree with interval-based encoding, and then isolates non-tree edges to generate the TRS structure for the graph. Shortest path queries between arbitrary nodes are performed upon this structure. The final result could be a detail path with exact path cost. This algorithm is quite easy to scale to large graphs, since the TRS algorithm automatically divide the graph into a set of connected components, each of which has a single TRS structure. Our experiments has proved that the algorithm fits large sparse graph quite well in real world.
This work is supported by a grant from “Special Research on Key Technology of Domestic Database with High Performance and High Security” for National Core-High-Base Major Project of China (No. 2010ZX01042-001-002-002).
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Wang, Q., Jin, J., Chen, H. (2013). TRS: A New Structure for Shortest Path Query. In: Zhou, S., Wu, Z. (eds) Social Media Retrieval and Mining. Communications in Computer and Information Science, vol 387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41629-3_2
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DOI: https://doi.org/10.1007/978-3-642-41629-3_2
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