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Cascading failures would have serious impact on a variety of important networks, and simulation in the accurate network model is the mainstream method to study the cascading failure. Maximum Entropy (ME) approach is widely applied to construct the network model with partial information available, but its complete network assumption is criticized for leading to impractical result. In this paper, ME approach is proved to be capable of building the accurate network model for cascading failure simulation in the dense weighted directed network. The China interbank network of listed banks is selected as research object, and maximum entropy approach is used to calculate the weight of each link in the interbank network according to the sum data. Then the agentbased computational experiments of cascading failure are launched on this model. Results show that the 5 largest banks can cause cascading failure, especially ICBC. When the asset loss rate threshold is reached, any one of the 5 banks failure could trigger a cascading failure to ruin the entire network. Above the threshold, loss rate is positively correlated with intensity of cascading failure and negatively correlated with duration. Simulation results coincide with the conclusion of financial supervision and regulation departments, and it proves that the maximum entropy approach applied to dense weighted directed networks practicably.

Cascading Failure in the Maximum Entropy Based Dense Weighted Directed Network: An Agent-based Computational Experiment