OpenHGNN/openhgnn/output/NARS

• NARS
• • How to run
  • candidate dataset
  • • description
  • performance
  • TrainerFlow: node_classification
  • • model
  • Hyper-parameter specific to the model
  • More
  • • • Contirbutor
      • If you have any questions,

NARS

Paper:Scalable Graph Neural Networks for Heterogeneous Graphs

Code from author:https://github.com/facebookresearch/NARS

How to run

Clone the Openhgnn-DGL

python main.py -m NARS -t node_classification -d acm4NARS -g 0 --use_best_config

Candidate dataset: acm4NARS

If you do not have gpu, set -gpu -1.

candidate dataset

acm4NARS

NOTE: NARS can handle mag oag dataset, we will add these two datasets in our further work.

description

• Number of nodes

  paper	4025
  author	17431
  field	73

• Number of edges

  paper-author	13407
  paper-field	4025

• Subsets: paper-author, paper-field

performance

Node classification

	accuracy
acm4NARS	0.93

TrainerFlow: node_classification

model

• NARS

  NARS is composed of WeightedAggregator and SIGN.

• WeightedAggregator

  Get new features by multiplying the old features by the weight matrix.

• SIGN

  The MLP classifier. It is composed of a several linear layers. Then project the node embeddings to the vector space to predict the type of the nodes.

Hyper-parameter specific to the model

R = 2
input_dropout = True
cpu_preprocess = True
ff_layer = 2

Best config can be found in best_config

More

Contirbutor

Tianyu Zhao, Yibo Li[GAMMA LAB]

If you have any questions,

Submit an issue or email to liushiliushi0@gmail.com.