Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) In order to use your own data, you have to provide an N by N adjacency matrix (N is the number of nodes) ...

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

Abstract: Attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep ...

On this basis, a two-dimensional data-driven convolutional neural network model (2DD-CNN) is proposed ... this method preserves the time series information in the form of matrix arrangement. At the ...

Abstract: Graph Neural Networks (GNNs) have been proven to be useful for learning graph-based knowledge. However, one of the drawbacks of GNN techniques is that they may get stuck in the problem of ...

By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning Network Want to learn ...