Forecasting managerial turnover through e-mail based social network analysis

In this study we propose a method based on e-mail social network analysis to compare the communication behavior of managers who voluntarily quit their job and managers who decide to stay. Collecting 18 months of e-mail, we analyzed the communication behavior of 866 managers, out of which 111 left a large global service company. We compared differences in communication patterns by computing social network metrics, such as betweenness and closeness centrality, and content analysis indicators, such as emotionality and complexity of the language used. To study the emergence of managers’ disengagement, we made a distinction based on the period of e-mail data examined. We observed communications during months 5 and 4 before managers left, and found significant variations in both their network structure and use of language. Results indicate that on average managers who quit had lower closeness centrality and less engaged conversations. In addition, managers who chose to quit tended to shift their communication behavior starting from 5 months before leaving, by increasing their degree and closeness centrality, the complexity of their language, as well as their oscillations in betweenness centrality and the number of “nudges” they need to send to peers before getting an answer.     

基于介中心性及K-shell的脑网络核心节点评价方法

在对基于核磁共振成像技术重构得到的人脑结构网络的研究中,核心节点的识别是对全脑网络特性展开研究的基础,具有重要意义。给出了一种基于K-shell和介中心性的核心节点评价方法,首先使用以节点局部重要性为标准的度中心性、邻近中心性和介中心性三个中心性评价方法分别对人脑结构网络中的节点重要性展开评估和分析;接着利用以节点全局地位为标准的K-shell分解法对人脑结构网络的核心节点展开分析。实验结果显示,由于同时兼顾了脑网络节点的整体特性和局部特性,该方法能够更全面和准确地识别核心脑区节点。     

Population-driven Urban Road Evolution Dynamic Model

In this paper, we propose a road evolution model by considering the interaction between population distribution and urban road network. In the model, new roads need to be constructed when new zones are built, and existing zones with higher population density have higher probability to connect with new roads. The relative neighborhood graph and a Fermat-Weber location problem are introduced as the connection mechanism to capture the characteristics of road evolution. The simulation experiment is conducted to demonstrate the effects of population on road evolution. Moreover, the topological attributes for the urban road network are evaluated using degree distribution, betweenness centrality, coverage, circuitness and treeness in the experiment. Simulation results show that the distribution of population in the city has a significant influence on the shape of road network, leading to a growing heterogeneous topology.     

一种基于最短路径介数的重要节点发现算法

网络中重要节点的发现是研究网络特性的重要方面之一,在复杂网络、系统科学、社会网分析和互联网搜索等领域中具有广泛的应用价值。为提高全网范围内重要节点发现的效率和有效性,提出了一种基于最短路径介数及节点中心接近度的重要节点发现算法,通过最短路径介数的方法确定全网内的重要节点,利用中心接近度分析重要节点的重要性。测试结果表明,与同类的系统比较起来,该方法具有比较好的性能。     

强/弱连接对软件网络结构的影响*

抽取类级软件网络拓扑模型,提出用紧密度来测度网络中的强连接,用结构洞来刻画网络中的弱连接。由紧密度与度的相关性得出高度值节点周围一定存在强连接,低度值节点间不一定用强连接相关联;由中介度与结构洞的相关性得出网络中的弱连接与节点占据结构洞有关,无洞和全洞结构都不存在弱连接;利用K-近邻指标分析强/弱连接的分布特征,得出强连接具有网络中心性,而弱连接的中心性并不明显;通过逐步删除一定百分比的强/弱连接节点和边来对比分析它们对软件网络结构的影响,结果表明移去强连接可迅速破坏网络的连通性,而移去弱连接在一定范围内对网络结构影响并不大,但随着删除比例增多,网络的连通性和信息流动受到严重破坏。所得结论对度量和控制软件结构复杂性提供理论指导。     

基于带权语言网络的网页关键词抽取

论述了网页文档带权语言网络的建立过程,给出了结合介数指标与紧密度指标的词语综合中心度度量方法,实验表明采用该方法的关键词抽取结果能够很好地符合网页主题。     

Organizational structure of Mazda’s Keiretsu: a graph theoretic analysis

According to Freeman, centrality indices, which include degree, closeness, and betweenness, are an effective way to measure organizational structure. However, Ito discussed the implications of centrality, and found that differences exist even when the centrality is the same. Using data on transactions and cross shareholdings that were drawn from Mazda’s Yokokai Keiretsu, a new method — the SNW model — is proposed to calculate structural importance. Therefore, this article provides a new perspective for discovering the structural importance of network organizations. Furthermore, implications based on statistical comparisons between the SNW model and corporate performance such as sales and profits are proffered.     

基于节点重要度的复杂网络可靠性研究

为研究复杂网络的节点重要度及其可靠性,在随机网络、小世界网络和无标度网络模型下,理论分析度值中心性、半局部中心性、介数中心性和PageRank算法在节点重要度评估上的准确性,通过仿真模拟方法,对网络理论模型和实际复杂网络分别进行研究,采用逐步移除节点和重复计算的方式,考察网络最大连通子图和节点移除比例的关系,同时对数值仿真结果进行合理分析。研究结果说明基于网络全局信息的介数中心性和PageRank算法对节点重要度评估的效果更好,同时说明网络的可靠性与网络拓扑结构有关,实际复杂网络一般同时具备多种拓扑特性。     

Using core-periphery structure to predict high centrality nodes in time-varying networks

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree. To measure the effectiveness of our framework, we perform prediction task on a large set of diverse time-varying networks. We obtain F1-scores as high as 0.81 and 0.72 in predicting the top m closeness and betweenness centrality vertices respectively for real networks where the highly central vertices mostly reside in the innermost core. For synthetic networks that conform to this property we achieve F1-scores of 0.94 and 0.92 for closeness and betweenness respectively. We validate our results by showing that the practical effects of our predicted vertices match the effects of the actual high centrality vertices. Finally, we also provide a formal sketch demonstrating why our method works.     

小世界网络抗毁性及介-度相关性研究

日常生活中,复杂网络主要面临随机攻击和选择性攻击,网络模型的结构特性使复杂网络在不同攻击方式下的抗毁性有很大的差异.综合考虑复杂网络的抗毁性参数和网络模型的结构特性差异,文章以节点的最大度值、平均度值、最大介数和平均介数为度量参数,通过5种不同攻击方式对WS小世界网络的抗毁性进行了测试,得到了介度关系曲线,随着节点的移除,动态分析了WS小世界网络的抗毁性和攻击下小世界网络的介 度相关性.实验结果表明,WS小世界网络在RD攻击下表现出一定的脆弱性,在其他攻击方式下有很强的鲁棒性,而其介度关系在某些区域呈线性关系,但大部分区域呈现无规律性,其动态相关性需要进一步研究.小世界网络较强的抗毁性对组建网络有重要的影响,鉴于小世界网络的结构特性,组建网络时应尽量不要把网络的功能集中在少数Hub节点上.针对WS小世界网络较强的鲁棒性,结合小世界网络的结构特性,将做进一步研究,以便提出更高效的攻击策略.     

An exploration of a quasi-stable online network: A longitudinal perspective

This study uses social capital theory and social network analysis to explore how relations in an online academic communication network impact the overall structure of that network. Social network analysis is a particularly advantageous methodological tool for linking changes in microlevel choices to macrolevel structural alterations. Results indicate the online communication network’s structure exists independent of changes in communication ties. Specifically degree centrality, betweenness centrality, and clique formation indicated communication ties are initiated and discontinued over a 7-month timeframe. However, the dynamic nature of the microlevel communication choices does not mirror the quasi-stable structure of the online network. Results provide avenues for future research.     

基于节点亲密度的重要性评估算法

识别复杂网络中的重要节点一直是社会网络分析和挖掘领域的热点问题,有助于理解有影响力的传播者在信息扩散和传染病传播中的作用。现有的节点重要性算法充分考虑了邻居信息,但忽略了邻居节点与节点之间的结构信息。针对此问题,考虑到不同结构下邻居节点对节点的影响力不同,提出了一种综合考虑节点的邻居数量和节点与邻居间亲密程度的节点重要性评估算法,其同时体现了节点的度属性和“亲密”属性。该算法利用相似性指标来测量节点间的亲密程度,以肯德尔相关系数为节点排序的准确度评价指标。在多个经典的实际网络上利用SIR(易感-感染-免疫)模型对传播过程进行仿真,结果表明,与度指标、接近中心性指标、介数中心性指标与K-shell指标相比,KI指标可以更精确地对节点传播影响力进行排序。     

A centrality analysis of the transaction relationships in Panasonic

Panasonic initiated a reform strategy called “Value Creation 21” in 2001. This strategy had a strong impact on its transaction relationships. This research covers one of the important issues in analyzing how the transaction network in Panasonic has changed during the period of “Value Creation 21.” In order to make Panasonic’s transaction relationships visible and countable, we have introduced graph theory and a measure centrality index from the viewpoints of degree, closeness, and betweenness by using data collected in 2002 and 2005. Our findings are reported here. First, the number of firms in Panasonic’s transaction network in 2005 was smaller than in 2002. Second, not only the degree, but also the closeness and betweenness, of the main firms in the Panasonic Group and their suppliers decreased a little more in 2005. Third, the number of in-degree firms declined, whereas the relative importance of Panasonic in the transaction network was more significant. Fourth, Panasonic’s affiliated firms in components & devices and the digital AVC network domain ranked higher than other firms in the transaction network. Last, its out-degree suppliers dropped more in 2005 than in 2002. With these findings, we finally concluded how Panasonic arranged its transaction relationships during the turnaround.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

基于社区的动态网络节点介数中心度更新算法

随着互联网技术的迅猛发展,社会网络呈现出爆炸增长的趋势,传统的静态网络分析方法越来越难以达到令人满意的效果,于是对网络进行动态分析就成为社会网数据管理领域的一个研究热点。节点介数中心度衡量的是一个节点对图中其他点对最短路径的控制能力,有利于挖掘社会网络中的重要节点。在图结构频繁变化的场合,若每次变化后都重新计算整个图中所有节点的介数中心度,则效率将会很低。针对动态网络中节点介数中心度计算困难的问题,本文提出一种基于社区的节点介数中心度更新算法。通过维护社区与社区、社区与节点的最短距离集合,快速过滤掉那些在网络动态更新中不受影响的点对,从而大大提高节点介数中心度的更新效率。真实数据集和合成数据集上的实验结果表明了论文所提算法的有效性。     

Centrality prediction in dynamic human contact networks

Real technological, social and biological networks evolve over time. Predicting their future topology has applications to epidemiology, targeted marketing, network reliability and routing in ad hoc and peer-to-peer networks. The key problem for such applications is usually to identify the nodes that will be in more important positions in the future. Previous researchers had used ad hoc prediction functions. In this paper, we evaluate ways of predicting a node’s future importance under three important metrics, namely degree, closeness centrality, and betweenness centrality, using empirical data on human contact networks collected using mobile devices. We find that node importance is highly predictable due to both periodic and legacy effects of human social behaviour, and we design reasonable prediction functions. However human behaviour is not the same in all circumstances: the centrality of students at Cambridge is best correlated both daily and hourly, no doubt due to hourly lecture schedules, while academics at conferences exhibit rather flat closeness centrality, no doubt because conference attendees are generally trying to speak to new people at each break. This highlights the utility of having a number of different metrics for centrality in dynamic networks, so as to identify typical patterns and predict behaviour. We show that the best-performing prediction functions are 25% more accurate on average than simply using the previous centrality value. These prediction functions can be efficiently computed in linear time, and are thus practical for processing dynamic networks in real-time.     

小世界网络中的删边扩容效应

不同于无标度网络,小世界网络中体现负载特性的介数分布和体现连接特征的度分布这两种结构差异很大,依据这个差异,我们提出了有效的删边扩容方法并进行了仿真实验,并进一步从容量与最大介数的关系理论分析确证了实验结果.接着,我们分析了小世界网络中介数分布的差异性与删边扩容效果的相关性,把基于介数的基尼系数变化用于度量删边扩容的效果,指出了结构差异是导致删边扩容效应的必要条件.还指出了无论从容量与介数的定义、仿真结果、结构差异与删边扩容效果的关系来看,都应该从介数入手来实现删边扩容方法,这种方法反直觉,有很多变种,并且实现方便、节省成本,作为一类新的路由策略可以广泛地应用于各类通信网络和交通网络等领域.     

基于复杂网络的轨道交通网络结构特性研究

本文首创性的运用复杂网络理论比较全面地分析了北京城市轨道交通网络的静态结构特性,动态加权结构特性.主要以2015年北京市地铁路网为基础,建立起轨道交通网络样本,通过计算度与度分布、平均路径长度、介数等网络统计特性分析了轨道交通网络的静态结构特性;并首次在静态网络拓扑的基础上结合了2015年北京地铁路网断面客流信息,通过计算点强度及其分布、节点加权介数、介数与点强度相关性等统计指标分析了网络的动态特性.从而解释了各个因素之间的相互作用和对北京轨道交通网络结构上的影响,为以后轨道交通的建设和运营提供了一些指导性的建议.     

Improving customer retention in financial services using kinship network information

This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable.     

基于Space-P复杂网络模型的城市公交网络特性分析

针对城市公交网络中换乘网络的整体性能分析问题,提出一种基于复杂网络理论的分析方法。首先,基于图论思想,将公交网络建模成由Space-P方法表示的公交换乘网络拓扑模型;然后,统计分析了公交换乘网络的度分布、平均最短路径长度、聚类系数、紧密中心性和介数中心性等特性。以北京市的公交网络为例进行了相关分析,从宏观角度说明北京公交网络具有小世界网络特点,市民出行需要换乘的概率较大,但换乘较为便捷;同时,给出了相关站点的具体地理信息,为公交规划部门优化公交网络提供了参考。