We introduce a model of information dissemination in signed networks. It is a discrete-time process in which uninformed actors incrementally receive information from their informed neighbors or from the outside. Our goal is to minimize the number of confused actors - that is, the number of actors who receive contradictory information. We prove upper bounds for the number of confused actors in signed networks and in equivalence classes of signed networks. In particular, we show that there are signed networks where, for any information placement strategy, almost 60\% of the actors are confused. Furthermore, this is also the case when considering the minimum number of confused actors within an equivalence class of signed graphs.

翻译：本文提出了一种符号网络中的信息传播模型。该模型是一个离散时间过程，其中未获知信息的行动者会逐步从其已获知信息的邻居或外部来源接收信息。我们的目标是最小化混淆行动者的数量——即接收到矛盾信息的行动者数量。我们证明了符号网络及符号网络等价类中混淆行动者数量的上界。特别地，我们证明了存在某些符号网络，对于任意信息放置策略，近60%的行动者都会产生混淆。此外，在符号图等价类中考虑混淆行动者的最小数量时，这一结论同样成立。