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摘要翻译：
语义记忆是人类记忆中存储概念或意义知识的子系统，而不是生活中特定的经验。概念在语义记忆中的组织可以理解为一个语义网络，其中概念（节点）根据感知、相似性等与其他概念相关联（链接）。词汇存取是这个系统的补充部分，允许对这些有组织的知识进行检索。虽然概念信息存储在特定的基础组织下（因此产生特定的拓扑结构），但准确地访问任何信息单元（例如概念）对于有效地检索语义信息以进行实时更新至关重要。信息检索过程的一个例子发生在言语流利性任务中，众所周知，它涉及两种不同的机制：聚类--或在子类别中生成单词，以及当子类别耗尽时--切换到新的子类别。我们将该方法推广到网络上的随机行走（聚类），并将其推广到以一定概率跳转（切换）到任意节点，并基于马尔可夫链导出了该方法的解析表达式。结果表明，这种双重机制有助于在平均首次通过时间方面优化不同网络模型的探索。此外，这一认知启发的双重机制为更好地理解和评价类似开关现象可行的其他复杂系统中的探索、传播和传输现象开辟了一个新的框架。
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英文标题：
《Switcher-random-walks: a cognitive-inspired mechanism for network
exploration》
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作者：
Joaqu\'in Go\~ni, I\~nigo Martincorena, Bernat Corominas-Murtra,
Gonzalo Arrondo, Sergio Ardanza-Trevijano, Pablo Villoslada
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Physics 物理学
二级分类：Disordered Systems and Neural Networks 无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life specific experiences. The organization of concepts within semantic memory can be understood as a semantic network, where the concepts (nodes) are associated (linked) to others depending on perceptions, similarities, etc. Lexical access is the complementary part of this system and allows the retrieval of such organized knowledge. While conceptual information is stored under certain underlying organization (and thus gives rise to a specific topology), it is crucial to have an accurate access to any of the information units, e.g. the concepts, for efficiently retrieving semantic information for real-time needings. An example of an information retrieval process occurs in verbal fluency tasks, and it is known to involve two different mechanisms: -clustering-, or generating words within a subcategory, and, when a subcategory is exhausted, -switching- to a new subcategory. We extended this approach to random-walking on a network (clustering) in combination to jumping (switching) to any node with certain probability and derived its analytical expression based on Markov chains. Results show that this dual mechanism contributes to optimize the exploration of different network models in terms of the mean first passage time. Additionally, this cognitive inspired dual mechanism opens a new framework to better understand and evaluate exploration, propagation and transport phenomena in other complex systems where switching-like phenomena are feasible.
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PDF链接：
https://arxiv.org/pdf/0903.4132
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