时变时滞细胞神网络的稳定性分析

本文研究了一类具有时变时滞的细胞神经网络的稳定性问题,利用新的Layapunov Krasovskii函数,给出了系统全局渐近稳定的时滞相关稳定性条件。其结果以线性矩阵不等式的形式给出,通过对矩阵特征值的应用,得到了一个新的LMI的应用形式,改善了LMI的应用。最后,数值算例说明了本文结果的优越性。     

关于移动自组网自安全证书管理模型

对目前建立移动自组网信任模型的各种主流方案进行了分类和评述,重点阐述了自安全证书管理模型各种方案的优缺点,并指出了今后的研究方向.     

基于共引网络理论的产业需求竞争网络

根据2002-2012年北京市投入产出的直接消耗系数,结合共引网络的建模思想,构建一类加权无向网络——产业需求竞争网络,并引入边权、单位权和差异性作为衡量网络中竞争关系的指标。通过研究发现:INCN-BJ网络的边权反映了产业链环节的完善程度,单位权反映了产品部门对产业系统的促进作用,差异性则区分出产业系统中需要加大产业政策指导的落后产品部门。     

网络环境下组织间界面规则的演进：团队建设的视角

组织间界面规则的演进是组织理论研究的一个重要问题。网络环境下组织间界面规则的演进就是团队的管理边界与社会心理边界通过漂移与重合运动所达到的一种相互平衡的关系结果,团队间界面规则的演进是一种内生性改变,是一个自组织的动态演进过程,团队间界面规则演进的“双元模式”和团队学习是推动组织界面规则动态演进的内在机理。     

创新型中小企业成长性评价与预测研究——基于我国创业板上市公司数据

创新型中小企业成长对经济增长、创新国家建设有重要的作用,科学评价其成长性有助于为规范和促进它们实现快速发展创造条件提供依据。在已有研究基础上,结合创新型中小企业主要特征,提出了创新型中小企业成长性评价体系。然后以我国创业板上市公司为样本,初步对企业成长性进行检验与评价。在此基础上,运用神经网络方法构建了创新型中小企业成长性预测模型。进而根据分析结果揭示导致企业成长主要差异原因,并提出了一些促进企业成长的建议。     

社会网络视角下农民工创业互助组织发展探析

农民工创业互助组织发展前景广阔,但面临诸多制约。从社会网络视角探讨这一新兴组织的发展方向及其与农民工创业计划的关系可以得出：社会网络越发达的农民工,创业行为越活跃;强关系是创业互助组织创立的基础,弱关系是决定其发展水平和规模的重要因素。同时,农民工创业互助行为受到社会支持和示范效应的影响。在组织发展的不同阶段,妥善利用社会网络中的强关系和弱关系,将情感型策略与工具型策略相结合,是不断推进农民工创业互助组织发展的动力。     

计算机网络问题的哲学断想——关于历史观的点滴思考

马克思主义哲学历史观作为对社会发展一般规律的正确反映,必须积极应对网络时代对生产方式、交往方式、人的价值与人的全面发展、网络伦理等历史观重大问题的挑战,认真研究和探索它们提出的新问题,以推动马克思主义哲学的新发展.     

The potential of online social networking data for augmenting the study of high-risk personal networks among young men who have sex with men at-risk for HIV

Capturing dynamics in high-risk personal networks is essential for preventing HIV transmission. Online social networking data offer incentive to augment traditional selfreported approaches for network enumeration. To explore what online networks reveal about dynamics among high-risk associates, we examine the relationship between egocentric confidant and sex networks and personal Facebook friendship networks of a cohort of young Black men who have sex with men. Although overlap exists between self-reported and Facebook associates, the stabilities of each were unrelated. Confidants who were also Facebook friends with a respondent were, however, more likely to be retained. Thus, Facebook networks contain stable confidants.     

k-means-based algorithm for blockmodeling linked networks

The paper presents a k-means-based algorithm for blockmodeling linked networks where linked networks are defined as a collection of one-mode and two-mode networks in which units from different one-mode networks are connected through two-mode networks. The reason for this is that a faster algorithm is needed for blockmodeling linked networks that can better scale to larger networks. Examples of linked networks include multilevel networks, dynamic networks, dynamic multilevel networks, and meta-networks. Generalized blockmodeling has been developed for linked/multilevel networks, yet the generalized blockmodeling approach is too slow for analyzing larger networks. Therefore, the flexibility of generalized blockmodeling is sacrificed for the speed of k-means-based approaches, thus allowing the analysis of larger networks. The presented algorithm is based on the two-mode k-means (or KL-means) algorithm for two-mode networks or matrices. As a side product, an algorithm for one-mode blockmodeling of one-mode networks is presented. The algorithm’s use on a dynamic multilevel network with more than 400 units is presented. A situation study is also conducted which shows that k-means based algorithms are superior to relocation algorithm-based methods for larger networks (e.g. larger than 800 units) and never much worse.     

Identifying Systemically Important Banks: A temporal approach for macroprudential policies

Contrary to the general belief, systemic risk does not only regard the risk posed by balance sheet relationships and interdependencies among institutions. It also features a temporal dimension related to the inappropriate responses of financial market participants to changes in risk over time. This paper proposes a method to simultaneously address the cross-sectional and the time dimension in which systemic risk materializes. The method is based on the TOPHITS algorithm. It provides three scores, namely borrowing, lending and time scores: the first two represent the systemic importance of the borrowing and the lending activity associated with each financial institution,while the third represents an empirical Early Warning Signal of the financial crisis. Our findings reveal that the identification of the time score as an indicator for an incoming market distress could be relevant to design macro prudential policies.