dgl/examples/pytorch/ogb/sign

• SIGN: Scalable Inception Graph Neural Network
• • Dependencies
  • How to run
  • • ogbn-products
    • ogbn-arxiv
    • ogbn-mag
  • Results

SIGN: Scalable Inception Graph Neural Network

Paper: https://arxiv.org/abs/2004.11198

Dependencies

• pytorch 1.5
• dgl 0.5 nightly build
  • pip install --pre dgl
• ogb 1.2.3

How to run

ogbn-products

python3 sign.py --dataset ogbn-products --eval-ev 10 --R 5 --input-d 0.3 --num-h 512 \
    --dr 0.4 --lr 0.001 --batch-size 50000 --num-runs 10

ogbn-arxiv

python3 sign.py --dataset ogbn-arxiv --eval-ev 10 --R 5 --input-d 0.1 --num-h 512 \
    --dr 0.5 --lr 0.001 --eval-b 100000 --num-runs 10

ogbn-mag

ogbn-mag is a heterogeneous graph and the task is to predict publishing venue
of papers. Since SIGN model is designed for homogeneous graph, we simply ignore
heterogeneous information (i.e. node and edge types) and treat the graph as a
homogeneous one. For node types that don't have input feature, we featurize them
with the average of their neighbors' features.

python3 sign.py --dataset ogbn-mag --eval-ev 10 --R 5 --input-d 0 --num-h 512 \
    --dr 0.5 --lr 0.001 --batch-size 50000 --num-runs 10

Results

Table below shows the average and standard deviation (over 10 times) of
accuracy. Experiments were performed on Tesla T4 (15GB) GPU on Oct 29.

Dataset	Test Accuracy	Validation Accuracy	# Params
ogbn-products	0.8052±0.0016	0.9299±0.0004	3,483,703
ogbn-arxiv	0.7195±0.0011	0.7323±0.0006	3,566,128
ogbn-mag	0.4046±0.0012	0.4068±0.0010	3,724,645