Structural cohesion and embeddedness in two-mode networks

The detection of structural cohesion is a key utility of social network analysis, but little work has been done to refine the detection of structural cohesion in two-mode networks. Most work on cohesion in two-mode networks either: (1) attempts to detect cohesion in such networks using one-mode projections (which can be problematic for reasons we discuss); or (2) focuses on restrictive substructures like bi-cliques to identify cohesive subgroups. We propose a new strategy for two-mode networks that follows the general reasoning of approaches to detecting structural cohesion in one-mode networks. Our approach identifies the number of actors from one node set that may be removed before disconnecting actors in the opposite set. We also develop a definition of embeddedness that draws on Moody and White’s hierarchical nesting approach.     

A polyhedral study of the maximum edge subgraph problem

The study of cohesive subgroups is an important aspect of social network analysis. Cohesive subgroups are studied using different relaxations of the notion of clique in a graph. For instance, given a graph and an integer k, the maximum edge subgraph problem consists in finding a k-vertex subset such that the number of edges within the subset is maximum. This work proposes a polyhedral approach for this NP-hard problem. We study the polytope associated to an integer programming formulation of the problem, present several families of facet-inducing valid inequalities, and discuss the separation problem associated to these families.     

More about empirical evaluation of formal theory†

The purpose of this discussion is to transform the implicit equilibrium assumption endemic to network analysis into an explicit instrument for such analysis. I propose a formal model that brings together Coleman's restriction of Walras’ general equilibrium model and recent developments in describing the “social topology” of a multiple network system of actors such that a class of relational equilibria is defined. The specific equilibrium expected in a system is a function of the previously existing stratification of actors in the system. Corresponding to multiple observed networks, the model generates multiple equilibrium networks. The structural analysis of the observed networks can therefore be repeated on the equilibrium networks so as to assess the extent to which the analysis would differ if the observed relations were actually in an equilibrium state. Numerical illustration is provided by an analysis of alternative relational equilibria in the system of elite experts in methodological and mathematical sociology as such a system existed in 1975.     

Closed nets in linear groups

The paper reviews the author’s results in the theory of elementary nets (carpets). In particular, closed (admissible) nets are investigated. For an elementary net (a net considered without the diagonal) of additive subgroups of an arbitrary commutative ring, the concepts of the derivative net, the closure of the net, and the net associated with the elementary group are introduced. Factorization of the elementary groups is proposed. This factorization is then used to construct an example of a closed (admissible) net which cannot be completed to a (complete) net. For a third-order elementary net σ of additive subgroups of a commutative ring, decomposition of an elementary transvection from the elementary group E(σ) is obtained.     

On the use of external information in social network analysis

Network analysis focuses on links among interacting units (actors). Interactions are often derived from the presence of actors at events or activities (two-mode network) and this information is coded and arranged in a typical affiliation matrix. In addition to the relational data, interest may focus on external information gathered on both actors and events. Our aim is to explore the effect of external information on the formation of ties by setting a strategy able to decompose the original affiliation matrix by linear combinations of data vectors representing external information with a suitable matrix of coefficients. This allows to obtain peculiar relational data matrices that include the effect of external information. The derived adjacency matrices can then be analyzed from the network analysis perspective. In particular, we look for groups of structurally equivalent actors obtained through clustering methods. Illustrative examples and a real dataset in the framework of scientific collaboration will give a major insight into the proposed strategy.     

Subgroups of the Chevalley Groups and Lie Rings Definable by a Collection of Additive Subgroups of the Initial Ring

The paper provides an overview of the authors’ results on nets, carpets, elementary net subgroups, carpet subgroups, and carpet Lie rings. Some open questions are also formulated.     

基于社会网络视角的突发灾害应急响应合作者关系网络分析——以“6·24”叠溪滑坡灾害为例

突发灾害应急管理实践表明,响应主体间的合作关系网络可靠性将影响应急响应的效率。本文以“6.24”新磨滑坡作为研究案例,采用文献分析、访谈、关系挖掘等研究方法构建灾害响应过程中主体间的合作者关系网络,重点从社会网络视角对该合作者关系的网络韧性进行量化分析,并对比随机生成的合作网络以及国外类似案例。研究发现,应急响应网络中的关键行动主体履行了救灾响应所要求的责任角色;在应急救援的效率方面,我国的应急救援体制具有制度优越性;指挥部等关键行动者会影响整个合作网络的效率和韧性。因此,在灾后应急救援时需进一步提升整体网络成员中协同救灾的水平,以在救援效率和效果上取得实效。     

The Role of Metanetworks in Network Evolution

The question of what structures of relations between actors emerge in the evolution of social networks is of fundamental sociological interest. The present research proposes that processes of network evolution can be usefully conceptualized in terms of a network of networks, or “metanetwork,” wherein networks that are one link manipulation away from one another are connected. Moreover, the geography of metanetworks has real effects on the course of network evolution. Specifically, both equilibrium and non-equilibrium networks located in more desirable regions of the metanetwork are found to be more probable. These effects of metanetwork geography are illustrated by two dynamic network models: one in which actors pursue access to unique information through “structural holes,” and the other in which actors pursue access to valid information by minimizing path length. Finally, I discuss future directions for modeling network dynamics in terms of metanetworks.     

The Emergence of Social Order by Processes of Typifying: A Computational Model

The article argues that Rational Choice approaches are not sufficient to explain the “how” of the emergence of social order. Therefore, the concept of “typifying” according to the theory of Berger and Luckmann is understood as the most important form of establishing social orders between different social actors. A computational model is described that captures the basic features of the typifying process. Each artifical actor consists of two different neural nets, an “action net” and a “perception net”. These nets allow an actor to establish social rules of interaction with other actors, to remember the other actors after a time and to typify new actors as persons that belong to the same type as actors the first actor is already “acquainted” with. Experimental results and theoretical consequences are also given.     

Is politics power or policy oriented? A comparative analysis of dynamic access models in policy networks*

We will show that these base models and some intermediate ones result in fundamentally different network structures and predicted outcomes. Moreover, we will show that the policy driven models do fundamentally better than the power driven models.

In policy networks actors use access relations to influence preferences of other actors. Establishment and shifts of access relations and their consequences for outcomes of decisions are the main focal points in this paper. Unlike most policy network studies, we therefore do not take the network and its relations as given and constant. Instead we device computer simulation models to account for the dynamics in policy networks. We compare different models and investigate the resulting network structures and predicted outcomes of decisions. The choice among the alternative models is made by their correspondence with empirical network structures and actual outcomes of decisions.

In our models, we assume that all relevant actors aim at policy outcomes as close as possible to their own preferences. Policy outcomes are determined by the preferences of the final decision makers at the moment of the vote. In general, only a small fraction of the actors takes part in the final vote. Most actors have therefore to rely on access relations for directly or indirectly shaping the preferences of the final decision makers. For this purpose actors make access requests to other actors. An access relation is assumed to be established if such a request is accepted by the other actor.

Access relations require time and resources. Actors are therefore assumed to be restricted in the number of access requests they can make and the number of requests they can accept Moreover, due to incomplete information and simultaneous actions by other actors, actors have to make simplifying assumptions in the selection of their “best” requests and learn by experience.

We device two base models that correspond to two basic views on the nature of political processes. In the first view politics is seen as power driven. Corresponding to this view, actors aim at access relations with the most powerful actors in the field. They estimate their likelihood of success by comparing their own resources with those of the target actors. Power also determines the order in which actors accept requests. In the second view, policy matters and actors roughly estimate the effects access relations might have on the outcome of decisions. Actors select requests to “bolster” their own preference as much as possible.     

The Dynamics of Closeness and Betweenness

Although both betweenness and closeness centrality are claimed to be important for the effectiveness of someone's network position, it has not been comprehensively studied which networks emerge if actors strive to optimize their centrality in the network in terms of betweenness and closeness. We study each of these centrality measures separately, but we also analyze what happens if actors value betweenness and closeness simultaneously. Network dynamics differ considerably in a scenario with either betweenness or closeness incentives compared with a scenario in which closeness and betweenness incentives are combined. There are not only more stable networks if actors’ betweenness and closeness are combined, but also these stable networks are less stylized.     

NETEST: Estimating a Terrorist Network's Structure—Graduate Student Best Paper Award, CASOS 2002 Conference

Since the events of September 11, 2001, the United States has found itself engaged in an unconventional and asymmetric form of warfare against elusive terrorist organizations. Defense and investigative organizations require innovative solutions that will assist them in determining the membership and structure of these organizations. Data on covert organizations are often in the form of disparate and incomplete inferences of memberships and connections between members. NETEST is a tool that combines multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network's structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network's size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose what if scenarios to destabilize a network and predict its evolution over time.     

Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings

Social action is situated in fields that are simultaneously composed of interpersonal ties and relations among organizations, which are both usefully characterized as social networks. We introduce a novel approach to distinguishing different network macro-structures in terms of cohesive subsets and their overlaps. We develop a vocabulary that relates different forms of network cohesion to field properties as opposed to organizational constraints on ties and structures. We illustrate differences in probabilistic attachment processes in network evolution that link on the one hand to organizational constraints versus field properties and to cohesive network topologies on the other. This allows us to identify a set of important new micro-macro linkages between local behavior in networks and global network properties. The analytic strategy thus puts in place a methodology for Predictive Social Cohesion theory to be developed and tested in the context of informal and formal organizations and organizational fields. We also show how organizations and fields combine at different scales of cohesive depth and cohesive breadth. Operational measures and results are illustrated for three organizational examples, and analysis of these cases suggests that different structures of cohesive subsets and overlaps may be predictive in organizational contexts and similarly for the larger fields in which they are embedded. Useful predictions may also be based on feedback from level of cohesion in the larger field back to organizations, conditioned on the level of multiconnectivity to the field.     

Finding Optimal Targets for Change Agents: A Computer Simulation of Innovation Diffusion

We introduce a diffusion of innovation model based on a network threshold approach. Realistic network and threshold data were gathered regarding the diffusion of new software tools within part of a large organization. Novel model features are a second threshold for innovation rejection and a memory that allows actors to take trends into account. Computer simulations produce expected outcomes, such as the S-shaped diffusion curve, but also diffusion breakdown and oscillations. We define and compute the quality of change agent targets in terms of the impact targeted actors have on the diffusion process. Our simulations reveal considerable variance in the quality of actors as change agent targets. Certain actors can be singled out as especially important to the diffusion process. Small changes in the distribution of thresholds and changes in some parameters, such as the sensitivity for trends, lead to significant changes in the target quality measure. To illustrate these interdependencies we outline how the impact of an actor targeted by a change agent spreads through the network. We thus can explain why a good change agent target does not necessarily need to be an opinion leader. Simulations comparing the effectiveness of randomly selected targets versus a group of good change agent targets indicate that the selection of good targets can accelerate innovation diffusion.     

Actor analysis methods and their use for public policy analysts

Public policy analysts use methods rooted in OR and systems analysis to support policy makers in their judgement. In doing so, most policy analysts recognize the value of a certain understanding of the role of actors in policy making processes. Different methods are available to aid such understanding and, although they all focus on actors, there are important differences between them. Insight into the range of available methods and their characteristics will thus help policy analysts to learn more about the potential and limitations involved in analyzing multi-actor processes. This article provides such an overview, based on the main requirements these methods should meet. This overview is used to discuss some of the implications for policy analysts who are interested in analyzing multi-actor processes, focusing specifically on trade-offs between analytic quality and practical usability.     

中国轻工业全要素生产率的空间溢出效应研究——基于空间计量与社会网络分析

基于Malmquist指数方法测算的中国轻工业全要素生产率的省域数据,运用空间计量方法和社会网络分析方法探讨轻工业全要素生产率的空间关联特征和空间溢出效应,从整体和区域的视角审视轻工业在区域间的协调发展。研究发现,区域轻工业全要素生产率的增长具有空间关联性,各地区轻工业联系紧密,其网络结构的整体性强,通透性高,稳定性好,且具有非对称可达性;轻工业的空间关联网络可划分为四个功能板块——主溢出板块、净溢出板块、经纪人板块和主受益板块,各个板块呈现明显的“阶梯型”溢出特征。     

Identity-based learning and segregation in social networks under different institutional environments

To study the evolution of segregation in social networks across systems embedded in different institutional environments, we develop an identity-based learning model where segregation is stochastically conditioned by the initial distribution of the actor’s attention to identity and the updating of this distribution over time. The updating process, which we call the process of mutual learning multiplier, is based on an actor’s success and failure experiences in tying with the same-subgroup and cross-subgroup actors. Results from a Monte Carlo simulation of the model show that the mutual learning multiplier produces disproportional relationships between the initial distribution of identity attention and the level of segregation in social networks. We also find that those relationships are affected by the actors’ attention to structural holes, rate of learning from experience, system size, and the identity heterogeneity of the system. Overall, the model provides insights into various dynamics of network structuration across time and space.     

A combinatorial theory of minimal social situations

A minimal social situation is a game‐like situation in which there are two actors, each of them has two possible actions, and both evaluate the outcomes of their joint actions in terms of two categories (say, ‘success’ and ‘failure'). By fixing actors and actions and varying ‘payoffs’ the set of 256 ‘configurations’ is obtained. This set decomposes into 43 ‘structural forms’, or equivalence classes with respect to the relation of isomorphism defined on it. This main theorem and other results concerning related configurations (minimal decision situations) are derived in this paper by means of certain tools of group theory. Some extensions to larger structures are proved in the Appendix. In the introductory section after a brief explanation of the meaning given to the terms ‘structure’ and ‘isomorphism’ in mathematics (Bourbaki) it is shown how these terms can be used to formalize the concept of ‘social form’.     

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.     

Optimal healthcare decisions: The importance of the covariates in cost-effectiveness analysis

This paper deals with the decision problem of choosing an optimal medical treatment, among M possible candidates, when the states of nature are the net benefit of the treatments, and regression models for the treatment cost and effectiveness are assumed. In this setting a crucial step in the analysis is the construction of the population subgroups sharing characteristics specified by the covariates, so that optimal decisions are now not for the whole population of patients but for patient population subgroups.We argue that the existing formulations of population subgroups in the literature are too rigid and unrealistic for real applications, and instead we formulate the population subgroups on the base of selected “influential” covariates. The Bayesian variable selector we use is an optimal one under the 0-1 loss function, which means choosing the subset of covariates having the highest posterior probabilities based on the so-called intrinsic priors, an objective Bayesian tool that exhibits an excellent performance.For each population subgroup we study the optimal Bayesian decisions for two different utility functions. One optimal decision is that obtained maximizing the expected net benefit, and the other maximizing the expected number of times that the treatment having the highest net benefit is chosen.Illustrations of the procedure for real data show that the subset of influential covariates may vary across treatments. Subgroup optimal treatments are derived and compared with those given by preceding methods.