Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information.
For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data.
Our model generates graphs one block of nodes and associated edges at a time.
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
A dual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs.
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.
The decoder is a simple fully connected network that is adapted to specific tasks, such as link prediction, signal generation on graphs and full graph and signal generation.
#2 best model for Link Prediction on Cora