|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
SOTA for Link Prediction on Cora
We then propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.
SOTA for Graph Classification on RE-M5K
In order to exploit topological information from graph data, we show how graph structures can be encoded in the so-called extended persistence diagrams computed with the heat kernel signatures of the graphs.
We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.
SOTA for Graph Classification on D&D
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
SOTA for Graph Classification on COLLAB