基于综合功率介数的主动配电网脆弱性分析

考虑到主动配电网在智能电网发展中的重要性以及在潮流分布与运行方式上的复杂性,进行了符合主动配电网运行实际的脆弱性分析。将主动配电网视为有向网络,将系统潮流分布方向作为网络邻接矩阵的方向矢量,从而得到考虑方向性的网络介数指标,以衡量各节点在网络连通性中的重要性。考虑发电机-负荷因素,分析功率在支路间的分布情况,提出功率因数指标,以评定各节点在功率传输中的作用。使用熵值法确定各指标权重,提出综合功率介数的概念,并对主动配电网的脆弱环节进行识别。以年为时间尺度,对网络脆弱性的年变化趋势进行研究,发现了较强的季节特性。使用添加了分布式电源与电池储能系统的IEEE 33节点系统,计算了网络在受到按脆弱性识别结果进行攻击时的失负荷率,验证了所提方法的正确性。     

An overview of dynamic anomaly detection in social networks via control charts

Detection of anomalies in social networks has always been a topic of interest in the realm of social network analysis. However, in recent years, researchers from industrial process monitoring area have applied their methods for monitoring social networks. In this review paper, we discuss social network monitoring approaches that apply control charts to detect anomalies in social networks. We mention the characteristics of the approaches and will shed some light on different topics that can be considered for further investigation by interested researchers.     

一种基于贡献矩阵的有向网络节点关键度计算方法

复杂网络中的关键节点,其重要程度一般要比非关键节点拥有更大影响力。目前已有的关键节点的关键度计算算法大多根据不同的衡量指标进行计算。针对适用于有向网络的关键节点挖掘算法较少且算法中不同衡量指标的结合不够严谨的情况,提出一种基于贡献矩阵的有向网络节点关键度计算算法。该算法通过贡献矩阵结合节点关联关系和节点的位置作为衡量节点关键度标准。在实验网络上的传播实验表明,相较于基于关联关系关键节点挖掘算法（RelaCentrality）来评估关键节点重要性,该算法在挖掘关键节点的过程中效率更高,并且所挖掘得到的关键节点在网络中对信息的传播更为广泛。     

基于Web服务的分布式计算模型研究

分析了典型的Web服务分布式计算中存在的问题,结合层次性的树形数据结构与Web服务语义描述技术,提出了一种基于应用领域的层次结构的Web服务目录系统,详细讨论了该系统下目录节点增加、删除和更新算法,并讨论了Web服务的发布与发现过程,在该服务目录系统中,服务的描述信息分布在各个应用领域节点上,能够克服典型的Web服务分布式计算中存在的问题.     

基于复杂网络分析的软件高危缺陷评估方法

为了评估软件缺陷的风险,提出了一种基于复杂网络分析的软件缺陷评估方法。该方法首先用一个网络模型表达程序实体之间的关系,将源代码中的方法抽象为节点,方法间的调用关系抽象为有向边,以此构造程序源代码网络;然后分别用介数算法和PageRank算法计算造成软件缺陷的方法节点在源代码全局网络中的地位,由此评估缺陷的风险高低。实验结果表明,该方法在评估内部高危缺陷时有较好的效果,有助于提高软件开发维护人员对一些隐蔽高危缺陷的关注度,进而为后续修复缺陷与软件演化提供有益的线索。     

考虑有效距离接近中心性的信息设备重要度评估

电网信息物理系统中,信息系统失效与遭受的攻击可能造成物理系统的能量损失。针对这一问题,文中提出了一种考虑有效距离接近中心性的信息设备重要度评估方法。首先,将信息系统结构抽象为有权有向的拓扑图,使之适用于量化评估。其次,根据信息系统对物理系统可靠性的影响大小,计算拓扑图中各有向边的权重。在此基础上,引入有效距离的概念,计算各个节点的有效距离接近中心性,实现从能量损失角度对信息设备重要度的量化评估。最后,对测试系统进行了信息设备重要度排序,验证了所提方法的有效性。     

The Centrality of the Australian Suburb: Mobility Challenges and Responses by Outer Suburban Residents in Melbourne

Living in the suburbs is founded on mobility, as residents move to the periphery of the city, commute to work and travel for shops, schools and leisure. While there have been numerous studies raising critical questions on the vulnerability of outer suburban residents to loneliness, financial and mortgage stress, the actual experiences and challenges posed by the dependence of suburban life on mobility is rarely singled out for attention. Through purposive sampling and then snowballing, eight outer suburban residents participated in photo-elicited interviewing to detail their lifeworlds and mobility experiences. Problems ranged from getting to work to accessing schools, shops and even neighbourhood services as the suburban car culture was embraced but also constrained patterns of movement. However, there was also an array of positive experiences and alternatives to these patterns, including the pleasures to be found in traffic jams and the suburban environment and local actions to establish new pathways and means of movement as well as set up local employment options. Mobility constraint thereby generated a range of unexpected strategies, as residents exercised their agency to shape their experiences, create alternatives and to build suburban centred lives.     

Are you connected? Evaluating information cascades in online discussion about the #RaceTogether campaign

In the context of online discussion about the recent Starbucks' “Race Together” cup campaign, this study aims to explore the central users in the online discussion network on Twitter and the factors contributing to a user's central status in the network. A social network analysis of 18,000 unique tweets comprising 26,539 edges and 14,343 Twitter users indicated five types of central users: conversation starter, influencer, active engager, network builder, and information bridge. Moreover, path analysis revealed that the number of people a Twitter user follows, the number of followers a user has, and the number of tweets a user generates within a time period helped a user increase his/her “indegree” connections in the network, which, together with one's “out-degree” connections in the network, propelled a user to become a central figure in the network.     

Incremental deployment of network monitors based on Group Betweenness Centrality

In many applications we are required to increase the deployment of a distributed monitoring system on an evolving network. In this paper we present a new method for finding candidate locations for additional deployment in the network. This method is based on the Group Betweenness Centrality (GBC) measure that is used to estimate the influence of a group of nodes over the information flow in the network. The new method assists in finding the location of k additional monitors in the evolving network, such that the portion of additional traffic covered is at least (1−1/e) of the optimal.     

Centrality and charisma: Comparing how leader networks and attributions affect team performance.

When leaders interact in teams with their subordinates, they build social capital that can have positive effects on team performance. Does this social capital affect team performance because subordinates come to see the leader as charismatic? We answered this question by examining 2 models. First, we tested the charisma-to-centrality model according to which the leader's charisma facilitates the occupation of a central position in the informal advice network. From this central position, the leader positively influences team performance. Second, we examined the centrality-to-charisma model according to which charisma is attributed to those leaders who are socially active in terms of giving and receiving advice. Attributed charisma facilitates increased team performance. We tested these 2 models in 2 different studies. In the first study, based on time-separated, multisource data emanating from members of 56 work teams, we found support for the centrality-to-charisma model. Formal leaders who were central within team advice networks were seen as charismatic by subordinates, and this charisma was associated with high team performance. To clarify how leader network centrality affected the emergence of charismatic leadership, we designed Study 2 in which, for 79 student teams, we measured leader networking activity and leader charisma at 2 different points in time and related these variables to team performance measured at a third point in time. On the basis of this temporally separated data set, we again found support for the centrality-to-charisma model. (PsycINFO Database Record (c) 2011 APA, all rights reserved)