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arXiv 1411.7838cs.DS
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寻找效应器的复杂性

The Complexity of Finding Effectors

Laurent Bulteau, Stefan Fafianie, Vincent Froese, Rolf Niedermeier, Nimrod Talmon

摘要:

有向图上的NP-难问题是由网络挖掘中的应用激发的,特别是关于社交网络中的概率信息传播过程的分析。在相应的模型中,弧携带概率,并且存在由相邻激活节点激活节点的概率扩散过程,其概率由弧指定。关键是要尽可能使用最少数量的“效应节点”来解释给定的网络激活状态;这些节点是在激活过程开始之前选择的。我们纠正,补充和扩展以前的工作,从数据采掘社区更彻底的计算复杂性分析,确定易处理和棘手的情况下。为此,我们还利用了一个参数化测量的“随机性程度”(“真正”的概率弧的数量),这可能被证明是有用的分析其他概率网络扩散问题以及。

英文摘要:

The NP-hard EFFECTORS problem on directed graphs is motivated by applications in network mining, particularly concerning the analysis of probabilistic information-propagation processes in social networks. In the corresponding model the arcs carry probabilities and there is a probabilistic diffusion process activating nodes by neighboring activated nodes with probabilities as specified by the arcs. The point is to explain a given network activation state as well as possible by using a minimum number of "effector nodes"; these are selected before the activation process starts. We correct, complement, and extend previous work from the data mining community by a more thorough computational complexity analysis of EFFECTORS, identifying both tractable and intractable cases. To this end, we also exploit a parameterization measuring the "degree of randomness" (the number of "really" probabilistic arcs) which might prove useful for analyzing other probabilistic network diffusion problems as well.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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