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61
The Effects of R&D Team Colocation on Communication Patterns among R&D, Marketing, and Manufacturing
 Management Science
, 1998
"... Reducing the physical distance among R&D engineers and between R&D and marketing is widely believed to result in more frequent communication, and hence higher product development performance. However, the empirical evidence for the effect of colocation on communication frequency is problema ..."
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Reducing the physical distance among R&D engineers and between R&D and marketing is widely believed to result in more frequent communication, and hence higher product development performance. However, the empirical evidence for the effect of colocation on communication frequency is problematic for two reasons: (1) the evidence often features either little contextual realism or doubtful internal validity, and (2) the analysis does not deal with the statistical problems typical of network data. Our study avoids the first problem by using sequential network data collected from a quasiexperiment at an industrial company that regrouped its R&D teams into a new facility. We avoid the second problem by using Wasserman and Iacobucci's (1988) method for the statistical analysis of sequential network data. Our results show that communication among R&D teams was enhanced after colocating these teams. Surprisingly, communication frequency between R&D and marketing was not affected by the increa...
Models for network evolution
 Journal of Mathematical Sociology
, 1996
"... Abstract: This paper describes mathematical models for network evolution when ties (edges) are directed and the node set is xed. Each of these models implies a speci c type of departure from the standard null binomial model. We provide statistical tests that, in keeping with these models, are sensit ..."
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Cited by 25 (4 self)
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Abstract: This paper describes mathematical models for network evolution when ties (edges) are directed and the node set is xed. Each of these models implies a speci c type of departure from the standard null binomial model. We provide statistical tests that, in keeping with these models, are sensitive to particular types of departures from the null. Each model (and associated test) discussed follows directly from one or more sociocognitive theories about how individuals alter the colleagues with whom they are likely to interact. The models include triad completion models, degree variance models, polarization and balkanization models, the HollandLeinhardt models, metric models, and the constructural model. We nd that many of these models, in their basic form, tend asymptotically towards an equilibrium distribution centered at the completely connected network (i.e., all individuals are equally likely to interact with all other individuals) � a fact that can inhibit the development of satisfactory tests. Keywords: triad completion, HollandLeinhardt model, polarization, degree variance, network evolution, constructuralism
Organization risk analyzer
, 2004
"... ORA is a network analysis tool that detects risks or vulnerabilities of an organization’s design structure. The design structure of an organization is the relationship among its personnel, knowledge, resources, and tasks entities. These entities and relationships are represented by the MetaMatrix. ..."
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Cited by 25 (15 self)
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ORA is a network analysis tool that detects risks or vulnerabilities of an organization’s design structure. The design structure of an organization is the relationship among its personnel, knowledge, resources, and tasks entities. These entities and relationships are represented by the MetaMatrix. Measures that take as input a MetaMatrix are used to analyze the structural properties of an organization for potential risk. ORA contains over 50 measures which are categorized by which type of risk they detect. Measures are also organized by input requirements and by output. ORA generates formatted reports viewable on screen or in log files, and reads and writes networks in multiple data formats to be interoperable with existing network analysis packages. In addition, it has tools for graphically visualizing MetaMatrix data and for optimizing a network’s design structure. ORA uses a Java interface for ease of use, and a C++ computational backend. The current version ORA 1.2 software is available on the CASOS
Identifying Cohesive Subgroups
 Social Networks
, 1995
"... Cohesive subgroups have always represented an important construct for sociologists who study individuals and organizations. In this article, I apply recent advances in the statistical modelling of social network data to the task of identifying cohesive subgroups from social network data. Further, th ..."
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Cohesive subgroups have always represented an important construct for sociologists who study individuals and organizations. In this article, I apply recent advances in the statistical modelling of social network data to the task of identifying cohesive subgroups from social network data. Further, through simulated data, I describe a process for obtaining the probability that a given sample of data could have been obtained from a network in which actors were no more likely to engage in interaction with subgroup members than with members of other subgroups. I obtain the probability for a specific data set, and then, through further simulations, develop a model which can be applied to future data sets. Also through simulated data, I characterize the extent to which a simple hillclimbing algorithm recovers known subgroup memberships. I apply the algorithm to data indicating the extent of professional discussion among teachers in a high school, and I show the relationship
How Homophily affects the Speed of Learning and Best Response Dynamics
, 2011
"... We examine how the speed of learning and bestresponse processes depends on homophily: the tendency of agents to associate disproportionately with those having similar traits. When agents ’ beliefs or behaviors are developed by averaging what they see among their neighbors, then convergence to a con ..."
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Cited by 24 (3 self)
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We examine how the speed of learning and bestresponse processes depends on homophily: the tendency of agents to associate disproportionately with those having similar traits. When agents ’ beliefs or behaviors are developed by averaging what they see among their neighbors, then convergence to a consensus is slowed by the presence of homophily, but is not influenced by network density. This is in stark contrast to the viral spread of a belief or behavior along shortest paths – a process whose speed is increasing in network density but does not depend on homophily. In deriving these results, we propose a new, general measure of homophily based on the relative frequencies of interactions among different groups.
Sampling algorithms for pure network topologies
, 2005
"... In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect t ..."
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In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (KDD). The intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term “pure ” topology types. In this paper, (1) we survey pure topology types along with existing sampling algorithms that generate them, (2) we introduce novel algorithms that enhance the diversity of samples, and address the case of cellular topologies, (3) we perform statistical studies of the stability of the properties of pure types to alternative generative algorithms, and a joint study of the separability of pure types, in terms of their embedding in a space of metrics for network analysis, widely adopted in the social and physical sciences. We conclude with a word of caution to the practitioners, who sample pure topology types to assess the “statistical significance” of their findings, e.g., the pvalue of the clustering coefficient is sensitive to the sampling algorithm used. We find that different pure types share similar topological properties. Further, real world networks hardly present the variability profile of a single pure type. We suggest the assumption of “mixtures of types ” as an alternative starting point for developing models and algorithms for network analysis.
A Tensor Spectral Approach to Learning Mixed Membership Community Models
"... Detecting hidden communities from observed interactions is a classical problem. Theoretical analysis of community detection has so far been mostly limited to models with nonoverlapping communities such as the stochastic block model. In this paper, we provide guaranteed community detection for a fam ..."
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Detecting hidden communities from observed interactions is a classical problem. Theoretical analysis of community detection has so far been mostly limited to models with nonoverlapping communities such as the stochastic block model. In this paper, we provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced in Airoldi et al. (2008). This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified approach to learning communities in these models via a tensor spectral decomposition approach. Our estimator uses loworder moment tensor of the observed network, consisting of 3star counts. Our learning method is based on simple linear algebraic operations such as singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters, and present a careful finite sample analysis of our learning method. Additionally, our results match the best known scaling requirements for the special case of the (homogeneous) stochastic block model.
A latent mixed membership model for relational data
 Proceedings of the 3rd international workshop on Link discovery
, 2005
"... Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patt ..."
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Cited by 18 (4 self)
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Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Our model explains the data using a small set of parameters that can be reliably estimated with an efficient inference algorithm. In our approach, the set of probabilistic assumptions may be tailored to a specific application domain in order to incorporate intuitions and/or semantics of interest. We demonstrate our methods on simulated data and we successfully apply our model to a data set of proteintoprotein interactions. Keywords latent mixedmembership, hierarchical mixture model, variational
A Bayesian approach toward finding communities and their evolutions in dynamic social networks
 In SDM’09: proceedings of the 2009 SIAM international
, 2009
"... Although a large body of work are devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolutions in a dynamic social ..."
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Cited by 14 (2 self)
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Although a large body of work are devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolutions in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and reallife data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several stateoftheart algorithms.
Algebraic Statistics for a Directed Random Graph Model with Reciprocation
"... The p1 model is a directed random graph model used to describe dyadic interactions in a social network in terms of effects due to differential attraction (popularity) and expansiveness, as well as an additional effect due to reciprocation. In this article we carry out an algebraic statistics analys ..."
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Cited by 8 (4 self)
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The p1 model is a directed random graph model used to describe dyadic interactions in a social network in terms of effects due to differential attraction (popularity) and expansiveness, as well as an additional effect due to reciprocation. In this article we carry out an algebraic statistics analysis of this model. We show that the p1 model is a toric model specified by a multihomogeneous ideal. We conduct an extensive study of the Markov bases for p1 models that incorporate explicitly the constraint arising from multihomogeneity. We consider the properties of the corresponding toric variety and relate them to the conditions for existence of the maximum likelihood and extended maximum likelihood estimator. Our results