摘要：The state space of logical networks is exponentially dependent on the number of nodes, which makes it challenging to deal with large-scale logical networks. In this paper, a brief review of existing methods in large-scale logical networks is firstly presented, including network aggregation, approximation and logical matrix factorization. Then, the network aggregation method is extended to study large-scale probabilistic logical networks (PLNs). Some new criteria are established for the robustness of positive-probability attractors of large-scale PLNs with one-bit function perturbation. Finally, the results are applied to analyze the robustness of positive-probability attractors in the neurotransmitter signaling pathway.
关键词：large-scale networkprobabilistic logical networknetwork aggregationfunction perturbationattractoralgebraic state space representation
