Estimating time-varying networks

By Mladen Kolar, Le Song, Amr Ahmed, Eric P. Xing. Intelligent Systems in Molecular Biology, 2008

321 citations recorded by the source corpus.

An evacuated and hermetically sealed bellows assembly has a bellows core assembly for mechanical movement versus pressure differential requirements. Mechanical attachment means are secured to the opposite ends of the bellows assembly. Vibrational optimization is provided to the bellows assembly to reduce predetermined frequencies.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f39090f2c53c4801908328ba85c8f007620c0d59

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Citations in the corpus

Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes (2013, background): “On the other hand, l-1 regularization has also been applied to estimate sparse matrices [9].”
A state-space mixed membership blockmodel for dynamic network tomography (2008, methodology): “Here we used the time-evolving networks of Drosophila melanogaster reverse-engineered by Kolar et al. (2010) from a genome-wide microarray time series of gene expressions using a novel computational algorithm based on 1 regularized kernel reweighting regression, which is detailed in a companion pape”
Dynamical Models and tracking regret in online convex programming (2013, background): “, 2009; Vaswani & Lu, 2010) or for time-varying networks (Snijders, 2001; Kolar et al., 2010); the associated algorithms, however, are typically batch methods poorly suited to large quantities of streaming data.”
Structure Learning in Graphical Modeling (2016, background): “Alternatively, Kolar et al. (2010) and Kolar & Xing (2012) use fused lasso penalties.”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “Especially Xing and his associates have recently introduced temporal exponential random graph models and time-varying networks to capture dynamics of networks (Hanneke and Xing 2006; Guo et al. 2007; Kolar et al. 2010).”
The joint graphical lasso for inverse covariance estimation across multiple classes (2011, methodology): “Some proposals have used time-series data to deﬁne time-varying networks in the context of continuous or binary data (Zhou et al. 2008, Song et al. 2009 a , Ahmed & Xing 2009, Kolar & Xing 2009, Song et al. 2009 b , Kolar et al. 2010).”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, background): “The weight function is usually non-negative and symmetric over t e (see e.g. Song, Kolar, and Xing 2009; Zhou, Laﬀerty, and Wasserman 2010b; Kolar, Song, Ahmed, and Xing 2010; Kolar and Xing 2009; Tao, Huang, Wang, Xi, and Li 2016; Chen and He 2015).”
Learning Networks of Heterogeneous Influence (2012, background): “In both synthetic and real cascade data, we show that our model can better recover the underlying diffusion network and drastically improve the estimation of the transmission functions among networked entities.”
Maximin effects in inhomogeneous large-scale data (2014, background): “Varying-coefficient models seem particularly attractive to capture shifts in underlying distributions if the data are recorded chronologically, and the approach has been extended to cope with more general estimation problems, including estimation of time-varying graphs [Kolar et al. (2010)].”
Uncover Topic-Sensitive Information Diffusion Networks (2013, background): “Ideas, tweets, styles, and online advertisements spread from person to person within social networks consisting of millions of entities and edges.”