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Revert Propagation: Who are responsible for a contagion initialization in a Diffusion Network?

机译:还原传播:谁负责扩散网络中的传染初始化?

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Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.
机译:数百万个故事在社交网络中转移,其中一些是恶意的。我们可以识别原本负责发起传播的源节点吗?如果是这样,他们什么时候开始传播?近年来研究了识别基于有限观察的传播来源的问题,因为它可以有助于降低早期检测的不需要感染引起的损害。在本文中,我们提出了一种用于查找将一条信息发起到网络中的节点的有效算法,并且还推断出启动时的时间。我们提出源区估计方法,唯一的,估计每个节点的源概率,然后选择使用具有理论保证的众所周知的贪婪方法最大化概率的源集。观察到的节点是检测到的节点,这些节点清楚地清楚地传播了网络中的指定的恶意信息,但是检测到小部分节点。隐藏的受感染节点是隐藏的,该节点传播了网络中的信息,但是它们尚未识别。在这个问题中,我们首先估计其他节点之间的最短路径,以观察每个传播跟踪的唯一。之后,我们通过优化损失函数,找到最佳节点作为隐藏节点之间的源集。我们通过网络的实际传播的实验表明了我们在检测真实来源的方法中的优势,并将最先进的方法从不到10%的方式推广前十个精度约为30%。

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