Recovering sparse conditional independence graphs from data is a fundamental
problem in machine learning with wide applications. A popular formulation of
the problem is an L1 regularized maximum likelihood estimation. Many convex
optimization algorithms have been designed to solve this formulation to recover the
graph structure. Recently, there is a surge of interest to learn algorithms directly
based on data, and in this case, learn to map empirical covariance to the sparse
precision matrix. However, it is a challenging task in this case, since the symmetric
positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in
learned algorithms, and a direct mapping from data to precision matrix may contain
many parameters. We propose a deep learning architecture, GLAD, which uses an
Alternating Minimization (AM) algorithm as our model inductive bias, and learns
the model parameters via supervised learning. We show that GLAD learns a very
compact and effective model for recovering sparse graphs from data.
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Learning Graph Structure from Convolutional Mixtures
Machine learning frameworks such as graph neural networks typically rely on a given, fixed
graph to exploit relational inductive biases and thus effectively learn from network data.
However, when said graphs are (partially) unobserved, noisy, or dynamic, the problem
of inferring graph structure from data becomes relevant. In this paper, we postulate a
graph convolutional relationship between the observed and latent graphs, and formulate
the graph structure learning task as a network inverse (deconvolution) problem. In lieu of
eigendecompositionbased spectral methods or iterative optimization solutions, we unroll and
truncate proximal gradient iterations to arrive at a parameterized neural network architecture
that we call a Graph Deconvolution Network (GDN). GDNs can learn a distribution of graphs
in a supervised fashion, perform link prediction or edgeweight regression tasks by adapting
the loss function, and they are inherently inductive as well as node permutation equivariant.
We corroborate GDN’s superior graph learning performance and its generalization to larger
graphs using synthetic data in supervised settings. Moreover, we demonstrate the robustness and representation power of GDNs on real world neuroimaging and social network datasets.
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 Award ID(s):
 1750428
 NSFPAR ID:
 10443080
 Date Published:
 Journal Name:
 Transactions on machine learning research
 ISSN:
 28358856
 Page Range / eLocation ID:
 129
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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