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41
Mixed membership stochastic block models for relational data with application to proteinprotein interactions
 In Proceedings of the International Biometrics Society Annual Meeting
, 2006
"... We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of authorrecipient email, and social networks. Analyzing such data with p ..."
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Cited by 182 (30 self)
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We develop a model for examining data that consists of pairwise measurements, for example, presence or absence of links between pairs of objects. Examples include protein interactions and gene regulatory networks, collections of authorrecipient email, and social networks. Analyzing such data with probabilistic models requires special assumptions, since the usual independence or exchangeability assumptions no longer hold. We introduce a class of latent variable models for pairwise measurements: mixed membership stochastic blockmodels. Models in this class combine a global model of dense patches of connectivity (blockmodel) and a local model to instantiate nodespecific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodels with applications to social networks and protein interaction networks.
A p* primer: logit models for social networks
 SOCIAL NETWORKS
, 1999
"... A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others wHolland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions ..."
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Cited by 51 (0 self)
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A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others wHolland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33–65 Ž with discussion.; Fienberg, S.E., Wasserman,
Recovering temporally rewiring networks: A modelbased approach
 in Proc. 24th Int. Conf. Machine learning
, 2007
"... A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant ..."
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Cited by 27 (5 self)
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A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. We present a class of hidden temporal exponential random graph models (htERGMs) to study the yet unexplored topic of modeling and recovering temporally rewiring networks from time series of node attributes such as activities of social actors or expression levels of genes. We show that one can reliably infer the latent timespecific topologies of the evolving networks from the observation. We report empirical results on both synthetic data and a Drosophila lifecycle gene expression data set, in comparison with a static counterpart of htERGM. 1.
Detecting communities and their evolutions in dynamic social networks  a Bayesian approach
 MACH LEARN
, 2010
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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 22 (3 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 20 (12 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
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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Cited by 19 (0 self)
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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...
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 16 (5 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