Associative knowledge networks are central in many areas of learning and teaching. One key problem in evaluating and exploring such networks is to find out its key items (nodes), sub-structures (connected set of nodes), and how the roles of sub-structures can be compared. In this study, we suggest an approach for analyzing associative networks, so that analysis is based on spreading activation and systemic states that correpond to the state of spreading. The method is based on the construction of diffusion-propagators as generalized systemic states of the network, for an exploration of the connectivity of a network and, subsequently, on generalized Jensen–Shannon–Tsallis relative entropy (based on Tsallis-entropy) in order to compare the states. It is shown that the constructed systemic states provide a robust way to compare roles of sub-networks in spreading activation. The viability of the method is demonstrated by applying it to recently published network representations of students’ associative knowledge regarding the history of science.

中文翻译：

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描述关联知识网络中扩散激活的系统状态：从关键项到基于相对熵的比较

联想知识网络在学与教的许多领域中都至关重要。评估和探索此类网络的一个关键问题是找出其关键项（节点），子结构（节点的连接集）以及如何比较子结构的作用。在这项研究中，我们提出了一种分析关联网络的方法，以便该分析基于传播激活和与传播状态相对应的系统状态。该方法基于将扩散传播器构造为网络的广义系统状态，以探索网络的连通性，然后依次基于广义Jensen-Shannon-Tsallis相对熵（基于Tsallis-熵）比较状态。结果表明，构建的系统状态提供了一种鲁棒的方式来比较子网在扩展激活中的作用。通过将该方法应用于最近发布的有关科学历史的学生相关知识的网络表示，证明了该方法的可行性。