Causal discovery is crucial to obtain a deep understanding of the actual mechanism behind the online social network, e.g., identifying the influential individuals and understanding the interaction among user behavior sequences. However, detecting causal directions and pruning causal redundancy of online social networks are still the great challenge of existing research. This paper proposed a constraint-based approach, minimal causal network (MCN), to mine hidden non-redundant causal relationships behind user behavior sequences. Under the MCN, the transfer entropy with the adaptive causal time lag is used to detect causal directions and find causal time lags, while a permutation-based significance test is proposed to prune redundant edges. Experiments on simulated data verify the effectiveness of our proposed method. We also apply our approach to real-world data from Sina Weibo and reveal some interesting discoveries.
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