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Minimum Spanning Tree on Uncertain Graphs

机译:不确定图上的最小生成树

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摘要

In recent years, lots of data in various domain can be represented and described by uncertain graph model, such as protein interaction networks, social networks, wireless sensor networks, etc. This paper investigates the most reliable minimum spanning tree problem, which aims to find the minimum spanning tree (MST) with largest probability among all possible MSTs on uncertain graphs. In fact, the most reliable MST is an optimal choice between stability and cost. Therefore it has wide applications in practice, for example, it can serve as the basic constructs in a telecommunication network, the link of which can be unreliable and may fail with certain probability. A brute-force method needs to enumerate all possible MSTs and the time consumption grows exponentially with edge size. Hence we put forward an approximate algorithm in O(d~2|V|~2), where d is the largest vertex degree and |V| is vertex size. We point out that the algorithm can achieve exact solution with expected probability at least (1 - (1/2)~((d+1)/2))~(|V|-1) and the expected approximation ratio is at least (1/2)~(d|V|) when edge probability is uniformly distributed. Our extensive experimental results show that our proposed algorithm is both efficient and effective.
机译:近年来,不确定的图形模型可以表示和描述各个领域的大量数据,例如蛋白质相互作用网络,社交网络,无线传感器网络等。本文研究了最可靠的最小生成树问题,旨在寻找最小的生成树问题。不确定图中所有可能的MST中概率最大的最小生成树(MST)。实际上,最可靠的MST是在稳定性和成本之间的最佳选择。因此,它在实践中具有广泛的应用,例如,它可以用作电信网络中的基本结构,其链路可能不可靠并且可能以一定的概率失败。蛮力方法需要枚举所有可能的MST,并且时间消耗随边缘大小呈指数增长。因此,我们在O(d〜2 | V |〜2)中提出了一种近似算法,其中d是最大顶点度,| V |是最大顶点度。是顶点大小。我们指出该算法可以以至少(1--(1/2)〜((d + 1)/ 2))〜(| V | -1)的期望概率来实现精确解,并且期望近似比至少为当边缘概率均匀分布时为(1/2)〜(d | V |)。我们广泛的实验结果表明,我们提出的算法既有效又有效。

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