GCN

Introduction

[paper]

Title: Semi-Supervised Classification with Graph Convolutional Networks

Authors: Thomas Kipf and Max Welling

Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Config

dataset_name: neurosat
load_split_dataset: True
feature_type: all_one
task: maxsat
task_type: lcg
task_level: graph
dataset_path: ./dataset/maxsat

model_settings:
  model: gcn
  num_layers: 32
  hidden_size: 128
  dropout_ratio: 0
  loss: binary_cross_entropy
  num_fc: 3
  sigmoid: True

scheduler_settings:
  scheduler: ReduceLROnPlateau
  patience: 10
  factor: 0.5
  mode: min

# train settings
valid_metric: acc
epochs: 100
lr: 0.0001
weight_decay: 1e-10
device: cuda:7
batch_size: 4

#log settings
log_file: ./log/gcn.log