Humans communicate using systems of interconnected stimuli or concepts—from language and music to literature and science—yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient ways that humans process this information. Here, we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system’s network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly connected modules—the two defining features of hierarchical organization. Together, these results suggest that many communication networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.

人类使用相互联系的刺激或概念系统（从语言，音乐到文学和科学）进行交流，但目前尚不清楚这些网络的结构如何（如果有的话）如何支持信息交流。尽管信息理论提供了量化系统生成的信息的工具，但是传统的度量标准并未解决人类处理该信息的低效方式。在这里，我们开发了一个分析框架来研究人类观察者感知到的系统生成的信息。我们通过实验证明，这种可感知的信息严重取决于系统的网络拓扑。将我们的框架应用于几个真实的网络，我们发现他们可以交流大量信息（具有较高的熵），并且可以高效地进行交流（保持与人类期望的低差异）。此外，我们表明，这种高效的通信出现在同时具有异构网络，高度集线器和集群，紧密连接的模块的网络中-这是分层组织的两个定义特征。总之，这些结果表明，许多通信网络受到信息传输压力的限制，并且这些压力选择了特定的结构特征。