38 papers with code ·
Graphs

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

GRAPH CLASSIFICATION LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION SENTIMENT ANALYSIS TEXT CLASSIFICATION

Graph data widely exist in many high-impact applications.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK MULTI-TASK LEARNING REPRESENTATION LEARNING

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.

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.

ICLR 2019 • benedekrozemberczki/CapsGNN •

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.

In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for 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

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING