Search Results for author:
Found 25 papers, 7 papers with code
Fair Information Spread on Social Networks with Community Structure
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.
A pseudo-likelihood approach to community detection in weighted networks
Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns.
Conformal Prediction for Network-Assisted Regression
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.
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging
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.
Latent space models for multiplex networks with shared structure
Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set.
Overlapping community detection in networks via sparse spectral decomposition
We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities.
Community models for networks observed through edge nominations
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.
Simultaneous prediction and community detection for networks with application to neuroimaging
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.
High-dimensional Gaussian graphical model for network-linked data
Graphical models are commonly used to represent conditional dependence relationships between variables.
Recovering shared structure from multiple networks with unknown edge distributions
In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks.
Graph-aware Modeling of Brain Connectivity Networks
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.
Hierarchical community detection by recursive partitioning
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.
Link prediction for egocentrically sampled networks
Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes.
Generalized linear models with low rank effects for network data
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.
Network classification with applications to brain connectomics
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.
Network cross-validation by edge sampling
While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem.
Estimating network edge probabilities by neighborhood smoothing
The estimation of probabilities of network edges from the observed adjacency matrix has important applications to predicting missing links and network denoising.
Community Detection in Networks with Node Features
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.
Estimating the number of communities in networks by spectral methods
Community detection is a fundamental problem in network analysis with many methods available to estimate communities.
Detecting Overlapping Communities in Networks Using Spectral Methods
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice.
On semidefinite relaxations for the block model
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.
Optimization via Low-rank Approximation for Community Detection in Networks
Community detection is one of the fundamental problems of network analysis, for which a number of methods have been proposed.
High-dimensional Mixed Graphical Models
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.
Pseudo-likelihood methods for community detection in large sparse networks
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.
The method of moments and degree distributions for network models
Probability models on graphs are becoming increasingly important in many applications, but statistical tools for fitting such models are not yet well developed.
Statistics Theory Statistics Theory
