GeniePath: Graph Neural Networks with Adaptive Receptive Paths

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals a...

Growing adaptive neural networks with graph grammars

This paper describes how graph grammars may be used to grow neural networks. The grammar facilitates a very compact and declarative description of every aspect of a neural architecture; this is important from a software/neural engineering point of view, since the descriptions are much easier to write and maintain than programs written in a high-level language , such as C++, and do not require p...

Adaptive Graph Convolutional Neural Networks

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking dat...

Neural Networks with Local Receptive

Local receptive eld neurons comprise such well-known and widely used unit types as radial basis function neurons and neurons with center-surround receptive eld. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive eld neurons we show that the VC dimension of these networks is superlinear. In ...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)