51 research outputs found
Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling
We propose a new exact method for shortest-path distance queries on
large-scale networks. Our method precomputes distance labels for vertices by
performing a breadth-first search from every vertex. Seemingly too obvious and
too inefficient at first glance, the key ingredient introduced here is pruning
during breadth-first searches. While we can still answer the correct distance
for any pair of vertices from the labels, it surprisingly reduces the search
space and sizes of labels. Moreover, we show that we can perform 32 or 64
breadth-first searches simultaneously exploiting bitwise operations. We
experimentally demonstrate that the combination of these two techniques is
efficient and robust on various kinds of large-scale real-world networks. In
particular, our method can handle social networks and web graphs with hundreds
of millions of edges, which are two orders of magnitude larger than the limits
of previous exact methods, with comparable query time to those of previous
methods.Comment: To appear in SIGMOD 201
Sublinear Distance Labeling
A distance labeling scheme labels the nodes of a graph with binary
strings such that, given the labels of any two nodes, one can determine the
distance in the graph between the two nodes by looking only at the labels. A
-preserving distance labeling scheme only returns precise distances between
pairs of nodes that are at distance at least from each other. In this paper
we consider distance labeling schemes for the classical case of unweighted
graphs with both directed and undirected edges.
We present a bit -preserving distance labeling
scheme, improving the previous bound by Bollob\'as et. al. [SIAM J. Discrete
Math. 2005]. We also give an almost matching lower bound of
. With our -preserving distance labeling scheme as a
building block, we additionally achieve the following results:
1. We present the first distance labeling scheme of size for sparse
graphs (and hence bounded degree graphs). This addresses an open problem by
Gavoille et. al. [J. Algo. 2004], hereby separating the complexity from
distance labeling in general graphs which require bits, Moon [Proc.
of Glasgow Math. Association 1965].
2. For approximate -additive labeling schemes, that return distances
within an additive error of we show a scheme of size for .
This improves on the current best bound of by
Alstrup et. al. [SODA 2016] for sub-polynomial , and is a generalization of
a result by Gawrychowski et al. [arXiv preprint 2015] who showed this for
.Comment: A preliminary version of this paper appeared at ESA'1
Optimal Remote Qubit Teleportation Using Node2vec
Much research work is done on implementing quantum teleportation and entanglement swapping for remote entanglement. Due to dynamical topological changes in quantum networks, nodes have to construct the shortest paths every time they want to communicate with a remote neighbour. But due to the entanglement failures remote entanglement establishment is still a challenging task. Also as the nodes know only about their neighbouring nodes computing optimal paths between source and remote nodes is time consuming too. In finding the next best neighbour in the optimal path between a given source and remote nodes so as to decrease the entanglement cost, deep learning techniques can be applied. In this paper we defined throughput of the quantum network as the maximum qubits transmitted with minimum entanglement cost. Much of research work is done to improve the throughput of the quantum network using the deep learning techniques. In this paper we adopted deep learning techniques for implementing remote entanglement between two non-neighbour nodes using remote qubit teleportation and entanglement swapping. The proposed method called Optimal Remote Qubit Teleportation outperforms the throughput obtained by the state of art approach
Cut Tree Construction from Massive Graphs
The construction of cut trees (also known as Gomory-Hu trees) for a given
graph enables the minimum-cut size of the original graph to be obtained for any
pair of vertices. Cut trees are a powerful back-end for graph management and
mining, as they support various procedures related to the minimum cut, maximum
flow, and connectivity. However, the crucial drawback with cut trees is the
computational cost of their construction. In theory, a cut tree is built by
applying a maximum flow algorithm for times, where is the number of
vertices. Therefore, naive implementations of this approach result in cubic
time complexity, which is obviously too slow for today's large-scale graphs. To
address this issue, in the present study, we propose a new cut-tree
construction algorithm tailored to real-world networks. Using a series of
experiments, we demonstrate that the proposed algorithm is several orders of
magnitude faster than previous algorithms and it can construct cut trees for
billion-scale graphs.Comment: Short version will appear at ICDM'1
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