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Minimum Spanning Tree on Uncertain Graphs

机译:不确定图的最小生成树

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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,各个领域的大量数据可以由不确定的图形模型表示和描述,例如蛋白质交互网络,社交网络,无线传感器网络等。本文调查了最可靠的最低生成树问题,旨在找到最小生成树(MST)具有在不确定图中所有可能MST的最大概率。事实上,最可靠的MST是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构造,其链接可能是不可靠的并且可能失败,具有一定的概率。蛮力方法需要枚举所有可能的MST,并且时间消耗呈指数呈指数级尺寸。因此,我们提出了一种在O(d〜2 | v |〜2)中的近似算法,其中d是最大的顶点和| v |顶点大小。我们指出,该算法可以至少使用预期概率实现精确的解决方案(1 - (1/2)〜((d + 1)/ 2))〜(| v | -1),并且预期近似比至少是(1/2)〜(d | v |)均匀分布时。我们广泛的实验结果表明,我们所提出的算法既有效又有效。

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