no code implementations • 15 May 2023 • Octavio Mesner, Elizaveta Levina, Ji Zhu
While some IM algorithms aim to remedy disparity in information coverage using node attributes, none use the empirical com- munity structure within the network itself, which may be beneficial since communities directly affect the spread of information.
no code implementations • 10 Mar 2023 • Andressa Cerqueira, Elizaveta Levina
Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns.
no code implementations • 20 Feb 2023 • Robert Lunde, Elizaveta Levina, Ji Zhu
An important problem in network analysis is predicting a node attribute using both network covariates, such as graph embedding coordinates or local subgraph counts, and conventional node covariates, such as demographic characteristics.
no code implementations • 27 May 2022 • Snigdha Panigrahi, Natasha Stewart, Chandra Sekhar Sripada, Elizaveta Levina
Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately.
2 code implementations • 28 Dec 2020 • Peter W. MacDonald, Elizaveta Levina, Ji Zhu
Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set.
1 code implementation • 20 Sep 2020 • Jesús Arroyo, Elizaveta Levina
We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities.
no code implementations • 9 Aug 2020 • Tianxi Li, Elizaveta Levina, Ji Zhu
We propose a general model for a class of network sampling mechanisms based on recording edges via querying nodes, designed to improve community detection for network data collected in this fashion.
1 code implementation • 5 Feb 2020 • Jesús Arroyo, Elizaveta Levina
Here we present a method for supervised community detection, aiming to find a partition of the network into communities that is most useful for predicting a particular response.
1 code implementation • 4 Jul 2019 • Tianxi Li, Cheng Qian, Elizaveta Levina, Ji Zhu
Graphical models are commonly used to represent conditional dependence relationships between variables.
no code implementations • 13 Jun 2019 • Keith Levin, Asad Lodhia, Elizaveta Levina
In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks.
no code implementations • 6 Mar 2019 • Yura Kim, Daniel Kessler, Elizaveta Levina
One challenge in analyzing such data is that inference at the individual edge level is not particularly biologically meaningful; interpretation is more useful at the level of so-called functional regions, or groups of nodes and connections between them; this is often called "graph-aware" inference in the neuroimaging literature.
no code implementations • 2 Oct 2018 • Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Koen Van den Berge, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina
This can be done with a simple top-down recursive partitioning algorithm, starting with a single community and separating the nodes into two communities by spectral clustering repeatedly, until a stopping rule suggests there are no further communities.
no code implementations • 12 Mar 2018 • Yun-Jhong Wu, Elizaveta Levina, Ji Zhu
Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes.
no code implementations • 18 May 2017 • Yun-Jhong Wu, Elizaveta Levina, Ji Zhu
Networks are a useful representation for data on connections between units of interests, but the observed connections are often noisy and/or include missing values.
1 code implementation • 27 Jan 2017 • Jesús D. Arroyo-Relión, Daniel Kessler, Elizaveta Levina, Stephan F. Taylor
Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes.
no code implementations • 14 Dec 2016 • Tianxi Li, Elizaveta Levina, Ji Zhu
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem.
1 code implementation • 29 Sep 2015 • Yuan Zhang, Elizaveta Levina, Ji Zhu
The estimation of probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising.
no code implementations • 3 Sep 2015 • Yuan Zhang, Elizaveta Levina, Ji Zhu
Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice.
no code implementations • 3 Jul 2015 • Can M. Le, Elizaveta Levina
Community detection is a fundamental problem in network analysis with many methods available to estimate communities.
no code implementations • 10 Dec 2014 • Yuan Zhang, Elizaveta Levina, Ji Zhu
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice.
1 code implementation • 21 Jun 2014 • Arash A. Amini, Elizaveta Levina
We put ours and previously proposed SDPs in a unified framework, as relaxations of the MLE over various sub-classes of the SBM, revealing a connection to sparse PCA.
no code implementations • 31 May 2014 • Can M. Le, Elizaveta Levina, Roman Vershynin
Community detection is one of the fundamental problems of network analysis, for which a number of methods have been proposed.
no code implementations • 9 Apr 2013 • Jie Cheng, Tianxi Li, Elizaveta Levina, Ji Zhu
While graphical models for continuous data (Gaussian graphical models) and discrete data (Ising models) have been extensively studied, there is little work on graphical models linking both continuous and discrete variables (mixed data), which are common in many scientific applications.
no code implementations • 10 Jul 2012 • Arash A. Amini, Aiyou Chen, Peter J. Bickel, Elizaveta Levina
Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks.
no code implementations • 23 Feb 2012 • Peter J. Bickel, Aiyou Chen, Elizaveta Levina
Probability models on graphs are becoming increasingly important in many applications, but statistical tools for fitting such models are not yet well developed.
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