面向社交媒体的直接因果网络发现算法

高维时序因果网络发现是社交媒体因果关系发现的重要问题。然而，现有的时序因果关系发现方法不能发现直接因果以致因果网络推断结果不准确。针对此问题提出了一种直接因果网络发现方法。该方法考虑了时序因果模型的因果延迟、滞后期数量和条件节点集等因素，更准确地发现直接因果关系；另外，采用结合置换检验的因果关系检验方法，解决传递熵阈值难以设定的问题。实验结果表明，该方法在因果网络推断中优于现有方法，有效提升时序上直接因果网络推断的准确率，适用于发现潜在社交媒体因果关系网络。

Direct causal network discovery algorithm for social media

Time-series causal discovery for high-dimensional networks have been increasingly significant in social media causality. However, the existing algorithms'' inability to discover direct causal relations renders the results of causal network inference not so accurate. Hence, this paper proposed a direct causal network discovery algorithm. It considered various factors, including the causal delay, the lag length and the conditional nodes sets, in the time-series causal model to help improve the accuracy of direct causal network inferring. Further, the method solved the difficulty of setting transfer entropy thresholds through permutation test. Experimental results demonstrate that the method outperforms the existing algorithms in causal network inferring and can conspicuously improve the accuracy of direct causal network inference on time series, which is suitable for discovering potential causal networks in social media.