Abstract: We investigate graph convolution networks with efficient learning from higher-order graph convolutions and direct learning from adjacency matrices for node classification. We revisit the ...

Abstract: Various computational problems can be reduced to computing the marginals and the partition function of a suitably defined standard factor graph (S-FG). The sum-product algorithm (SPA) is an ...

This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper: Thomas N. Kipf, Max Welling, ...

GraphSAINT is a general and flexible framework for training GNNs on large graphs. GraphSAINT highlights a novel minibatch method specifically optimized for data with complex relationships (i.e., ...

Department of Mathematics, Computer Science, and Engineering Technology, Elizabeth City State University, Elizabeth City, NC, USA. The paper is organized as follows. Section 2 establishes notation and ...

Graphene is a two-dimensional material consisting of a single layer of carbon atoms arranged in a honeycomb structure. Its properties include high strength and good conductivity of heat and ...

Algebra can often feel intimidating, filled with strange symbols and abstract concepts that seem hard to grasp. But with the right strategies, anyone can unlock its logic and see how algebra connects ...