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59
ESTIMATING TIMEVARYING NETWORKS
, 2010
"... Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward es ..."
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Cited by 55 (12 self)
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Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating timevarying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating timevarying networks, which both build on a temporally smoothed l1regularized logistic regression formalism that can be cast as a standard convexoptimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated timevarying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.
Recovering timevarying networks of dependencies in social and biological studies
 Proc. Nat. Acad. Sci
, 2009
"... A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. While there is a rich literature in modeling static or temporally invaria ..."
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Cited by 53 (9 self)
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A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. While there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this paper, we present a new machine learning method called TESLA, which builds on a temporally smoothed l1regularized logistic regression formalism that can be cast as a standard convexoptimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated timevarying networks, and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over more than 4000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency.
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 43 (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.
Supervised Link Prediction Using Multiple Sources
"... Abstract—Link prediction is a fundamental problem in social network analysis and modernday commercial applications such as Facebook and Myspace. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information. However, i ..."
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Cited by 20 (3 self)
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Abstract—Link prediction is a fundamental problem in social network analysis and modernday commercial applications such as Facebook and Myspace. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary social networks and/or derived proximity networks available. The contribution of the paper is twofold: (1) a supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks; (2) a feature design scheme for constructing a rich variety of pathbased features using multiple sources, and an effective feature selection strategy based on structured sparsity. Extensive experiments on three realworld collaboration networks show that our model can effectively learn to predict new links using multiple sources, yielding higher prediction accuracy than unsupervised and singlesource supervised models. Index Terms—social network; link prediction; multiple sources; supervised learning; I.
MAXIMUM LIKELIHOOD ESTIMATION FOR SOCIAL NETWORK DYNAMICS
 SUBMITTED TO THE ANNALS OF APPLIED STATISTICS
, 2009
"... A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuoustime Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The mo ..."
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Cited by 19 (6 self)
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A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuoustime Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.
Nonstationary dynamic Bayesian networks
 Advances in Neural Information Processing Systems 21
, 2009
"... A principled mechanism for identifying conditional dependencies in timeseries data is provided through structure learning of dynamic Bayesian networks (DBNs). An important assumption of DBN structure learning is that the data are generated by a stationary process—an assumption that is not true in m ..."
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Cited by 15 (0 self)
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A principled mechanism for identifying conditional dependencies in timeseries data is provided through structure learning of dynamic Bayesian networks (DBNs). An important assumption of DBN structure learning is that the data are generated by a stationary process—an assumption that is not true in many important settings. In this paper, we introduce a new class of graphical models called nonstationary dynamic Bayesian networks, in which the conditional dependence structure of the underlying datageneration process is permitted to change over time. Nonstationary dynamic Bayesian networks represent a new framework for studying problems in which the structure of a network is evolving over time. We define the nonstationary DBN model, present an MCMC sampling algorithm for learning the structure of the model from timeseries data under different assumptions, and demonstrate the effectiveness of the algorithm on both simulated and biological data. 1
Modeling the coevolution of behaviors and social relationships using mobile phone data
 IN PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS MULTIMEDIA, ACM, 2011
, 2011
"... The coevolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty closely tracking the everyday life of a complete community. We offer evidence that relationships and behavior coevolve in a student dormi ..."
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Cited by 14 (4 self)
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The coevolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty closely tracking the everyday life of a complete community. We offer evidence that relationships and behavior coevolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the coevolution in terms of the rates at which residents visit places and friends.
Dynamic egocentric models for citation networks
 In Proc. 28th Intl. Conf. on Machine Learning
, 2011
"... The analysis of the formation and evolution of networks over time is of fundamental importance to social science, biology, and many other fields. While longitudinal network data sets are increasingly being recorded at the granularity of individual timestamped events, most studies only focus on coll ..."
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Cited by 13 (4 self)
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The analysis of the formation and evolution of networks over time is of fundamental importance to social science, biology, and many other fields. While longitudinal network data sets are increasingly being recorded at the granularity of individual timestamped events, most studies only focus on collapsed crosssectional snapshots of the network. In this paper, we introduce a dynamic egocentric framework that models continuoustime network data using multivariate counting processes. For inference, an efficient partial likelihood approach is used, allowing our methods to scale to large networks. We apply our techniques to various citation networks and demonstrate the predictive power and interpretability of the learned statistical models. 1.
Review of statistical network analysis: models, algorithms, and software
 STATISTICAL ANALYSIS AND DATA MINING
, 2012
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