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Found 7 papers, 6 papers with code
The Forward-Backward Embedding of Directed Graphs
In particular, two nodes having many common successors in the graph tend to be represented by close vectors in the embedding space.
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning
The task of semi-supervised classification aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds.
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL.
A Consistent Diffusion-Based Algorithm for Semi-Supervised Classification on Graphs
Semi-supervised classification on graphs aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds.
Spectral embedding of regularized block models
Spectral embedding is a popular technique for the representation of graph data.
Variational Recurrent Neural Networks for Graph Classification
We address the problem of graph classification based only on structural information.
Ranked #28 on
Graph Classification
on NCI1
A Simple Baseline Algorithm for Graph Classification
Graph classification has recently received a lot of attention from various fields of machine learning e. g. kernel methods, sequential modeling or graph embedding.
Ranked #29 on
Graph Classification
on PTC
