• Corpus ID: 237431452

Dynamic Network Regression

@inproceedings{Zhou2021DynamicNR,
  title={Dynamic Network Regression},
  author={Yidong Zhou and Hans-Georg Muller},
  year={2021}
}
Network data are increasingly available in various research fields, motivating statistical analysis for populations of networks where a network as a whole is viewed as a data point. Due to the non-Euclidean nature of networks, basic statistical tools available for scalar and vector data are no longer applicable when one aims to relate networks as outcomes to Euclidean covariates, while the study of how a network changes in dependence on covariates is often of paramount interest. This motivates… 
1 Citations
Clustered Graph Matching for Label Recovery and Graph Classification
—Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the

References

SHOWING 1-10 OF 52 REFERENCES
Averages of unlabeled networks: Geometric characterization and asymptotic behavior
TLDR
The lack of vertex labeling results in a nontrivial geometry for the space of unlabeled networks, which in turn is found to have important implications on the types of probabilistic and statistical results that may be obtained and the techniques needed to obtain them.
Hypothesis Testing For Network Data in Functional Neuroimaging
TLDR
This work draws on concepts and techniques from geometry, and high-dimensional statistical inference, for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project, and shows that this global test is more statistical powerful, than a mass-univariate approach.
Fréchet regression for random objects with Euclidean predictors
Increasingly, statisticians are faced with the task of analyzing complex data that are non-Euclidean and specifically do not lie in a vector space. To address the need for statistical methods for
Populations of Unlabeled Networks: Graph Space Geometry and Geodesic Principal Components
TLDR
Viewing Graph Space as the quotient of a Euclidean space with respect to a finite group action, it is shown that it is not a manifold, and that its curvature is unbounded from above.
Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.
TLDR
This paper provides a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks, gene networks and brain networks.
Fréchet analysis of variance for random objects
Fréchet mean and variance provide a way of obtaining a mean and variance for metric space-valued random variables, and can be used for statistical analysis of data objects that lie in abstract
Finding community structure in networks using the eigenvectors of matrices.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2006
TLDR
A modularity matrix plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations, and a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong are proposed.
Manifold valued data analysis of samples of networks, with applications in corpus linguistics
TLDR
A general framework for extrinsic statistical analysis of samples of networks, motivated by networks representing text documents in corpus linguistics is developed and applied to the set of novels by Jane Austen and Charles Dickens.
Public transport networks: empirical analysis and modeling
TLDR
A simple model reproduces many of the identified PTN properties by growing networks of attractive self-avoiding walks, including a surprising geometrical behavior with respect to the two-dimensional geographical embedding and an unexpected attraction between transport routes.
Fréchet integration and adaptive metric selection for interpretable covariances of multivariate functional data
TLDR
This work defines the Frechet integral, which depends on the metric chosen for the space of covariance matrices, and demonstrates that ordinary integration is a special case where the Frobenius metric is used.
...
1
2
3
4
5
...