诺贝尔物理学奖得主知识交流网络结构研究

将诺贝尔物理学奖得主作为结点，在有知识交流的两位得主间添加一条连接，由此得到诺贝尔物理学奖得主知识交流网络。本文对此网络的结构特征进行研究，结果表明该网络的结构具有一般社会网络共有的Seale-free、度协调、负相关特征，而直径较一般社会网络大，聚集系数则明显较小。     

Computational organizational network modeling: Strategies and an example

This paper articulates the logic of computational organizational modeling as a strategy for theory construction and testing in the field of organizational communication networks. The paper introduces, Blanche, and objectoriented simulation environment that supports quantitative modeling and analysis of the evolution of organizational networks. Blanche relies on the conceptual primitives of attributes that describe network nodes and links that connect these nodes. Difference equations are used to model the dynamic properties of the network as it changes over time. This paper describes the design of Blanche and how it supports both the process of theory construction, model building and analysis of results. The paper concludes with an empirical example, to test the predictions of a network-based social influence model for the adoption of a new communication technology in the workplace.     

Some Simple Algorithms for Structural Comparison

Structural comparison (i.e., the simultaneous analysis of multiple structures) is a problem which arises frequently in such diverse arenas as the study of organizational forms, social network analysis, and automated text analysis. Prior work has demonstrated the applicability of a range of standard multivariate analysis procedures to the structural comparison problem. Here, some simple algorithms are provided which elucidate several of these methods in an easily implemented form. Carter T. Butts is Assistant Professor at the University of California-Irvine in the Department of Sociology, and is a member of the Institute for Mathematical Behavioral Sciences and the California Institute for Telecommunications and Information Technology. His current research focuses on communication during disasters, Bayesian inference for network data, network comparison, and the structure of spatially embedded interpersonal networks. Kathleen M. Carley is Professor at Carnegie Mellon University, with appointments in the Institute for Software Research International, the H.J. Heinz III School of Public Policy and Management, and the Department of Engineering and Public Policy. Her research centers around areas of social, organizational, knowledge and information networks, organizational design, change, adaptivity and and performance, computational organization theory, crisis management, social theory, impacts on information diffusion of changes in social policy and changes in communication technology, and mapping experts' and executives' knowledge networks using textual analysis techniques.     

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

The advent of social media has provided an extraordinary, if imperfect, ‘big data’ window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users’ average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar's number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Finding the needles in the haystack: efficient intelligence processing

As a result of communication technologies, the main intelligence challenge has shifted from collecting data to efficiently processing it so that relevant, and only relevant, information is passed on to intelligence analysts. We consider intelligence data intercepted on a social communication network. The social network includes both adversaries (eg terrorists) and benign participants. We propose a methodology for efficiently searching for relevant messages among the intercepted communications. Besides addressing a real and urgent problem that has attracted little attention in the open literature thus far, the main contributions of this paper are two-fold. First, we develop a novel knowledge accumulation model for intelligence processors, which addresses both the nodes of the social network (the participants) and its edges (the communications). Second, we propose efficient prioritization algorithms that utilize the processor’s accumulated knowledge. Our approach is based on methods from graphical models, social networks, random fields, Bayesian learning, and exploration/exploitation algorithms.     

Going Beyond the Data: Empirical Validation Leading to Grounded Theory

The purpose of this study is two-fold. First, a validation study on Construct-TM is conducted to show that modeling the actual and cognitive knowledge networks of a group can produce agent interactions within the model that correlate significantly with the communication network obtained from empirical data. Second, empirically grounded theory is produced by combining empirical data with simulation experiments run on empirically validated models.     

The Effect of Organizational Communication Media on Organizational Culture and Performance: An Agent-Based Simulation Model

This paper examines the mutual relationship between the communication richness of media used for conducting organizational communication and organizational culture. The richness of the media influences how well the organization might maintain its culture. On the other hand, a strong organizational culture allows a more effective use of the media by providing members with some of the necessary common ground to better understand the information exchanged. These relationships are investigated using an agent-based simulation model (ABM). Our ABM incorporates many partial theories into a coherent and fully defined model, which helps formalize and integrate those theories. Our model allows us to analyze non-linearities and interaction effects, which are difficult to investigate using other techniques. Additionally, the ABM allows us to investigate the dynamics of the phenomenon and generate hypotheses that could then be tested using empirical studies. Given the substantial resources necessary to conduct empirical studies, we think that the present ABM is valuable in helping guide data collection efforts. In this paper, we present results that show that organizational culture can influence the effectiveness of the media used for organizational communication and that a high media richness can help maintain and stabilize a culture. The effect of media richness on organizational culture depends on the initial strength of the culture. In general, for a given richness of the media, an initially strong culture stabilizes faster and becomes stronger through time than an initially weak culture. Additionally, the model suggests that a stable network of contacts among agents fosters a high achievement of organizational tasks. Conversely, when agents are forced to establish contacts with agents outside the usual network for doing their work, the accomplishment of tasks decreases.     

Can tools help unify organization theory? Perspectives on the state of computational modeling

Scholars engaged in the study of work group and organizational behavior are increasingly calling for the use of integrated methods in conducting research, including the wider adoption of computational models for generating and testing new theory. Our review of the state of modern computational modeling incorporating social structures reveals steady increases in the incorporation of dynamic, adaptive, and realistic behaviors of agents in network settings, yet exposes gaps that must be addressed in the next generation of organizational simulation systems. We compare 28 models according to more than two hundred evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model flexibility, communication channels, simulation types, knowledge, transactive memory, task complexity, and resource networks. Our survey assesses trends across the wide set of criteria, discusses practical applications, and proposes an agenda for future research and development. Michael J. Ashworth is a doctoral candidate in computational organization science at Carnegie Mellon University, where he conducts research on social, knowledge, and transactive memory networks along with their effects on group and organizational learning and performance. Practical outcomes of his work include improved understanding of the impact of technology, offshoring, and turnover on organizational performance. Mr. Ashworth has won several prestigious grants from the Sloan Foundation and the National Science Foundation to pursue his research on transactive memory networks. Journals in which his research has appeared include Journal of Mathematical Sociology, International Journal of Human Resource Management, and Proceedings of the International Conference on Information Systems. His recent work on managing human resource challenges in the electric power industry has been featured in the Wall Street Journal and on National Public Radio's ``Morning Edition.' Mr. Ashworth received his undergraduate degree in systems engineering from the Georgia Institute of Technology. Kathleen M. Carley is a professor at the Institute for Software Research International in the School of Computer Science at Carnegie Mellon University. She is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS), a university-wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu). Prof. Carley carries out research that combines cognitive science, dynamic social networks, text processing, organizations, social and computer science in a variety of theoretical and applied venues. Her specific research areas are computational social and organization theory; dynamic social networks; multi-agent network models; group, organizational, and social adaptation, and evolution; statistical models for dynamic network analysis and evolution, computational text analysis, and the impact of telecommunication technologies on communication and information diffusion within and among groups. Prof. Carley has undergraduate degrees in economics and political science from MIT and a doctorate in sociology from Harvard University.     

Network formation and knowledge gains

Incentive-based models for network formation link micro actions to changes in network structure. Sociologists have extended these models on a number of fronts, but there remains a tendency to treat actors as homogenous agents and to disregard social theory. Drawing upon literature on the strategic use of networks for knowledge gains, we specify models exploring the co-evolution of networks and knowledge gains. Our findings suggest that pursuing transitive ties is the most successful strategy, as more reciprocity and cycling result from this pursuit, thus encouraging learning across the network. We also discuss the role of network size, global network structure, and parameter strength in actors’ attainment of knowledge resources.     

面向集群创新网络异质企业的知识扩散建模及仿真研究

基于合作的集群创新网络知识扩散已经成为企业实现知识创新的重要手段,而集群创新网络知识扩散的动力学过程强烈依赖于异质企业间知识扩散能力的影响,为此,本文综合考虑了企业间不同接触数量、知识吸收和传播能力、知识淘汰率等异质性因素,建立了基于传染病理论的知识扩散模型,验证了由各异质因素构成的知识扩散再生数对知识扩散均衡和扩散效果的影响,并结合仿真实验进一步得出:在知识扩散前期,集群创新网络应发挥hub节点及异质网络的优势加快知识扩散,在中后期应注意企业关系发展的均衡性及企业接触邻居的规模性;相比过于强调知识交流的广泛性,加强企业传播能力和吸收能力的培养对网络知识扩散效果的提升更具意义;随着时间演化,企业知识淘汰率也会影响网络知识扩散的收敛情况。     

Exploring communication networks to understand organizational crisis using exponential random graph models

In recent social network studies, exponential random graph (ERG) models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the ERG models and demonstrate its use in modelling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modelling on communication networks, and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of ERG models, we find significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. We also notice that the attribute of hierarchical positions of actors (i.e., high rank versus low rank staff) have impact on the evolution process of networks during crisis. These findings could be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis.     

Knowledge management in semantic social networks

An object-oriented model of semantic social networks is proposed and formally analyzed. Methods for project, role, and team management based on the semantic model are defined and implemented in $\mathfrak{n}\mathfrak{i}\mathbf{K}\mathfrak{l}\mathfrak{a}\mathfrak{s}$ , a semantic wiki language based on frame logic developed by the author. The new approach to semantic social networks allows dynamic change of social network semantics and the establishment of the well known fishnet organization in a social network. In the end possible applications to knowledge management are presented.     

Post-merger cultural integration from a social network perspective: a computational modeling approach

Although cultural integration, or sharing a common corporate culture, is crucial for the success of mergers, previous studies have been limited to firm-level analyses. From a social network perspective, this study explores how cultural integration emerges from the patterns of social interactions among individuals. Using an agent-based model, we investigate the impact of network structures within and between two merging firms on post-merger cultural integration and organizational dysfunctions—individual turnover, interpersonal conflict and organizational communication ineffectiveness—that arise from insufficient cultural integration. The simulation results demonstrate that the highest level of cultural integration is achieved when social ties are more centralized within each merging firm and the social ties between the merging firms are less concentrated on central individuals. Additionally, the results show that within-firm and between-firm network structures significantly affect individual turnover, interpersonal conflict and organizational communication ineffectiveness, and that these three outcome measurements do not vary in tandem.     

The Other Side of the Network Coin: Cost Considerations of Network Structure

The purpose of this study is to investigate the differential impact that inter-organizational network connections have on organizational level change. Drawing from the strategic leaning perspective on adaptation, this study investigates how the nature of inter-organizational ties among top management impact the cost and the effectiveness of an organizational level change process. To build on the existing empirical work in this area, this study employs a virtual experiment to create a controlled laboratory investigation of the hypothesized relationships among the strength, formalization, and functional equivalence of network ties; and the cost and effectiveness of an organizational change process. The findings of this study provide support for the strength of weak ties argument and structural hole theory, in addition to suggesting a caveat to Galbraith's information processing model. Furthermore, the results reveal that the tradeoff between increasing effectiveness and decreasing costs is not universally applicable across all decisions regarding network structure.     

Stochastic optimization on social networks with application to service pricing

In this paper we develop a combined simulation and optimization approach for solving difficult decision problems on complex dynamic networks. For a specific reference problem we consider a telecommunication service provider who offers a telecommunication service to a market with network effects. More particularly, the service consumption of an individual user depends on both idiosyncratic characteristics and the popularity of this service among the customer’s immediate neighborhood. Both the social network and the individual user preferences are largely heterogeneous and changing over time. In addition the service provider’s decisions are made in absence of perfect knowledge about user preferences. The service provider pursues the strategy of stimulating the demand by offering differentiated prices to the customers. For finding the optimal pricing we apply a stochastic quasi-gradient algorithm that is integrated with a simulation model that drives the evolution of the network and user preferences over time. We show that exploiting the social network structure and implementing differentiated pricing can substantially increase the revenues of a service provider operating on a social network. More generally, we show that stochastic gradient methods represent a powerful methodology for the optimization of decisions in social networks.     

Intra-organizational Networks and Performance: A Review

Intra-organizational network research had its first heyday during the empirical revolution in social sciences before World War II when it discovered the informal group within the formal organization. These studies comment on the classic sociological idea of bureaucracy being the optimal organization. Later relational interest within organizational studies gave way to comparative studies on the quantifiable formal features of organizations. There has been a resurgence in intra-organizational networks studies recently as the conviction grows that they are critical to organizational and individual performance. Along with methodological improvements, the theoretical emphasis has shifted from networks as a constraining force to a conceptualization that sees them as providing opportunities and finally, as social capital. Because of this shift it has become necessary not only to explain the differences between networks but also their outcomes, that is, their performance. It also implies that internal and external networks should no longer be treated separately.Research on differences between intra-organizational networks centers on the influence of the formal organization, organizational demography, technology and environment. Studies on outcomes deal with diffusion and adaptation of innovation; the utilization of human capital; recruitment, absenteeism and turnover; work stress and job satisfaction; equity; power; information efficiency; collective decision making; mobilization for and outcomes of conflicts; social control; profit and survival of firms and individual performance.Of all the difficulties that are associated with intra-organizational network research, problems of access to organizations and incomparability of research findings seem to be the most serious. Nevertheless, future research should concentrate on mechanisms that make networks productive, while taking into account the difficulties of measuring performance within organizations, such as the performance paradox and the halo-effect.     

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovation potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Gained insight can ultimately help policy-makers to design GPS environments and promote innovation.     

Institutional Logics from the Aggregation of Organizational Networks: Operational Procedures for the Analysis of Counted Data

We address some problems of network aggregation that are central to organizational studies. We show that concepts of network equivalence (including generalizations and special cases of structural equivalence) are relevant to the modeling of the aggregation of social categories in cross-classification tables portraying relations within an organizational field (analogous to one-mode networks). We extend our results to model the dual aggregation of social identities and organizational practices (an example of a two-mode network). We present an algorithm to accomplish such dual aggregation. Within the formal and quantitative framework that we present, we emphasize a unified treatment of (a) aggregation on the basis of structural equivalence (invariance of actors within equivalence sets), (b) the study of variation in relations between structurally equivalent sets, and (c) the close connections between aggregation within organizational networks and multi-dimensional modeling of organizational fields.     

A longitudinal study of evolving networks in response to natural disaster

In this study, we present a longitudinal analysis of the evolution of interorganizational disaster coordination networks (IoDCNs) in response to natural disasters. There are very few systematic empirical studies which try to quantify the optimal functioning of emerging networks dealing with natural disasters. We suggest that social network analysis is a useful method for exploring this complex phenomenon from both theoretical and methodological perspective aiming to develop a quantitative assessment framework which could aid in developing a better understanding of the optimal functioning of these emerging IoDCN during natural disasters. This analysis highlights the importance of utilizing network metrics to investigate disaster response coordination networks. Results of our investigation suggest that in disasters the rate of communication increases and creates the conditions where organizational structures need to move at that same pace to exchange new information. Our analysis also shows that inter-organizational coordination network structures are not fixed and vary in each period during a disaster depending on the needs. This may serve the basis for developing preparedness among agencies with an improved perspective for gaining effectiveness and efficiency in responding to natural disasters.