We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we …
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5 results for “GPnn”
The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighboursstat.ML
Develops a theoretical framework for scalable Gaussian Process regression methods.
problem Limited scalability of Gaussian Process regression for large datasets.
method Introduces and analyzes Nearest Neighbour Gaussian Process (NNGP) and scalable GPnn methods.
result Derives almost sure pointwise limits for predictive criteria and proves risk minimax rates.
Study reveals decurve flows in graph propagation models.
problem Limitations of traditional graph analysis and propagation mechanisms.
method Introduces Generalized Propagation Neural Networks (GPNNs) and Continuous Unified Ricci Curvature (CURC).
result Observation of decurve flow during training of graph neural networks, revealing propagation dynamics.
New GPnn method achieves scalable regression with low computational cost.
problem Inefficient Gaussian Process (GP) regression for large datasets.
method GP nearest-neighbour (GPnn) prediction with robustness and limiting behaviour exploration.
result GPnn achieves high MSE accuracy with minimal parameter estimation effort, even in gross misspecification.
The paper analyzes convergence rates of Gaussian process approximations for scalable regression.
problem Characterizing convergence rates of Gaussian process approximations for scalable regression.
method Analysis of kernel functions and dataset-size for isotropic kernels like Matérn and squared-exponential.
result Upper and lower bounds on predictive MSE and calibration metric convergence rates are derived.