Malicious attacks extremely traumatize knowledge networks due to increasing interdependence among knowledge elements. Therefore, exposing the damage of malicious attacks to knowledge networks has important theoretical and practical significance. Despite the insights being offered by the growing research stream, few studies discuss the diverse responses of knowledge networks’ robustness to different target-attacks, and the authors lack sufficient knowledge of which forms of malicious attacks constitute greater disaster when knowledge networks evolve to different stages. Given the irreversible consequences of malicious attacks on knowledge networks, this paper aims to examine the impacts of different malicious attacks on the robustness of knowledge networks.,On the basic of dividing malicious attacks into six forms, the authors incorporate two important aspects of robustness of knowledge networks – structure and function – in a research framework, and use maximal connected sub-graphs and network efficiency, respectively, to measure structural and functional robustness. Furthermore, the authors conceptualize knowledge as a multi-dimensional structure to reflect the heterogeneous nature of knowledge elements, and design the fundamental rules of simulation. NetLogo is used to simulate the features of knowledge networks and their changes of robustness as they face different malicious attacks.,First, knowledge networks gradually form more associative integrated structures with evolutionary progress. Second, various properties of knowledge elements play diverse roles in mitigating damage from malicious attacks. Recalculated-degree-based attacks cause greater damage than degree-based attacks, and structure of knowledge networks has higher resilience against ability than function. Third, structural robustness is mainly affected by the potential combinatorial value of high-degree knowledge elements, and the combinatorial potential of high-out-degree knowledge elements. Forth, the number of high in-degree knowledge elements with heterogeneous contents, and the inverted U-sharp effect contributed by high out-degree knowledge elements are the main influencers of functional robustness.,The authors use the frontier method to expose the detriments of malicious attacks both to structural and functional robustness in each evolutionary stage, and the authors reveal the relationship and effects of knowledge-based connections and knowledge combinatorial opportunities that contribute to maintaining them. Furthermore, the authors identify latent critical factors that may improve the structural and functional robustness of knowledge networks.,First, from the dynamic evolutionary perspective, the authors systematically examine structural and functional robustness to reveal the roles of the properties of knowledge element, and knowledge associations to maintain the robustness of knowledge networks. Second, the authors compare the damage of six forms of malicious attacks to identify the reasons for increased robustness vulnerability. Third, the authors construct the stock, power, expertise knowledge structure to overcome the difficulty of knowledge conceptualization. The results respond to multiple calls from different studies and extend the literature in multiple research domains.

中文翻译：

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恶意攻击对知识网络健壮性的影响：基于多代理的模拟

恶意攻击由于知识要素之间越来越相互依赖，极大地伤害了知识网络。因此，暴露恶意攻击对知识网络的危害具有重要的理论和现实意义。尽管越来越多的研究提供了越来越多的见识，但是很少有研究讨论知识网络对不同目标攻击的鲁棒性的不同反应，并且作者缺乏足够的知识来了解当知识网络发展到不同阶段时，哪种形式的恶意攻击会构成更大的灾难。 。鉴于恶意攻击对知识网络的不可逆转后果，本文旨在研究各种恶意攻击对知识网络的健壮性的影响。在将恶意攻击分为六种形式的基础上，作者在研究框架中纳入了知识网络健壮性的两个重要方面-结构和功能，并分别使用最大连通子图和网络效率来衡量结构健壮性和功能健壮性。此外，作者将知识概念化为多维结构，以反映知识元素的异质性，并设计模拟的基本规则。NetLogo用于模拟知识网络的特征及其在面对不同恶意攻击时的健壮性变化。首先，知识网络随着进化的发展逐渐形成更多的关联性集成结构。其次，知识元素的各种属性在减轻恶意攻击造成的损害方面发挥着不同的作用。与基于度的攻击相比，基于度的重新计算攻击造成的破坏更大，并且知识网络的结构比功能具有更高的抵御能力。第三，结构鲁棒性主要受高级知识元素的潜在组合价值和高级知识元素的组合潜力的影响。第四，具有高度异质性的高学历知识元素的数量以及高学历知识元素造成的倒U形效应是影响功能鲁棒性的主要因素。在每个进化阶段都对结构和功能的健壮性进行恶意攻击，作者揭示了基于知识的联系和有助于维持它们的知识组合机会的关系和影响。此外，作者确定了可能改善知识网络的结构和功能健壮性的潜在关键因素。首先，从动态进化的角度，作者系统地研究了结构和功能健壮性以揭示知识元素和知识的属性的作用。协会以维持知识网络的健壮性。其次，作者比较了六种形式的恶意攻击的破坏程度，以确定增强鲁棒性漏洞的原因。第三，作者构建了股票，权力，专业知识结构以克服知识概念化的困难。