Cascades of information, ideas, rumors, and viruses spread through networks. Sometimes, it is desirable to find the source of a cascade given a snapshot of it. In this paper, source inference problem is tackled under Independent Cascade (IC) model. First, the #P-completeness of source inference problem is proven. Then, a Markov chain Monte Carlo algorithm is proposed to find a solution. It is worth noting that our algorithm is designed to handle large networks. In addition, the algorithm does not rely on prior knowledge of when the cascade started. Finally, experiments on real social network are conducted to evaluate the performance. Under all experimental settings, our algorithm identified the true source with high probability.

中文翻译：

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网络中的级联源推理：马尔可夫链蒙特卡洛方法。

信息，思想，谣言和病毒的级联通过网络传播。有时，希望给定级联的快照以找到级联的源。在本文中，在独立级联（IC）模型下解决了源推理问题。首先，证明了源推理问题的＃P-完备性。然后，提出了马尔可夫链蒙特卡罗算法来寻找解决方案。值得注意的是，我们的算法旨在处理大型网络。另外，该算法不依赖于级联何时开始的先验知识。最后，在真实社交网络上进行了实验以评估性能。在所有实验设置下，我们的算法都极有可能识别出真正的来源。