利用粗图训练图神经网络实现网络对齐

钱峰, 张蕾, 赵姝, 陈洁

Training of graph neural networks on coarsening graphs for network alignment

Feng Qian, Lei Zhang, Shu Zhao, Jie Chen

表3 douban online/offline数据集上FAROS算法与对比算法的

P r e c i s i o n @ K

对比

Table 3

P r e c i s i o n @ K

of FAROS and baselines on the douban online/offline dataset

$P r e c i s i o n @ K$	1	5	10	15	20	25	30
REGAL	0.0447	0.1360	0.2030	0.2576	0.3068	0.3435	0.3739
GAlign	0.4419	0.6780	0.7800	0.8327	0.8685	0.8909	0.9114
NAWAL	0.0000	0.0027	0.0036	0.0072	0.0089	0.0107	0.0179
WAlign	0.2039	0.3882	0.4946	0.5653	0.6136	0.6547	0.6816
Grad⁃Align	0.4794	0.7084	0.7737	0.8113	0.8318	0.8515	0.8649
FAROS	0.5358	0.7549	0.8435	0.8873	0.9106	0.9204	0.9275