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4 allmv/imdb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 4

P r e c i s i o n @ K

of FAROS and baselines on the allmv/imdb dataset

$P r e c i s i o n @ K$	1	5	10	15	20	25	30
REGAL	0.0951	0.2685	0.3868	0.4672	0.5280	0.5682	0.6049
GAlign	0.6982	0.8184	0.8549	0.8725	0.8858	0.8968	0.9061
NAWAL	0.0002	0.0002	0.0008	0.0014	0.0019	0.0027	0.0035
WAlign	0.5384	0.7112	0.7720	0.8053	0.8261	0.8420	0.8551
Grad⁃Align	0.7081	0.9370	0.9600	0.9654	0.9708	0.9728	0.9745
FAROS	0.7233	0.8516	0.8878	0.9065	0.9192	0.9279	0.9353