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

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

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

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 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

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