# 多标签结点分类

这是一个根据无监督的多标签结点分类设置而构建的排行榜,我们在几个真实的数据集上运行CogDL上的无监督表示学习算法,并将输出的表示作为经L2归一化的逻辑回归中的训练数据,计算并按照Micro-F1的大小进行排序。

Rank Method PPI (50%) Wikipedia (50%) Blogcatalog (50%) DBLP (5%) Flickr (5%)
1 NetMF (Qiu et al, WSDM'18) (opens new window) 23.73 ± 0.22 57.42 ± 0.56 42.47 ± 0.35 56.72 ± 0.14 36.27 ± 0.17
2 ProNE (Zhang et al, IJCAI'19) (opens new window) 24.60 ± 0.39 56.06 ± 0.48 41.14 ± 0.26 56.85 ± 0.28 36.56 ± 0.11
3 NetSMF (Qiu et at, WWW'19) (opens new window) 23.88 ± 0.35 53.81 ± 0.58 40.62 ± 0.35 59.76 ± 0.41 35.49 ± 0.07
4 Node2vec (Grover et al, KDD'16) (opens new window) 20.67 ± 0.54 54.59 ± 0.51 40.16 ± 0.29 57.36 ± 0.39 36.13 ± 0.13
5 LINE (Tang et al, WWW'15) (opens new window) 21.82 ± 0.56 52.46 ± 0.26 38.06 ± 0.39 49.78 ± 0.37 31.61 ± 0.09
6 DeepWalk (Perozzi et al, KDD'14) (opens new window) 20.74 ± 0.40 49.53 ± 0.54 40.48 ± 0.47 57.54 ± 0.32 36.09 ± 0.10
7 Spectral (Tang et al, Data Min Knowl Disc (2011)) (opens new window) 22.48 ± 0.30 49.35 ± 0.34 41.41 ± 0.34 43.68 ± 0.58 33.09 ± 0.07
8 Hope (Ou et al, KDD'16) (opens new window) 21.43 ± 0.32 54.04 ± 0.47 33.99 ± 0.35 56.15 ± 0.22 28.97 ± 0.19
9 GraRep (Cao et al, CIKM'15) (opens new window) 20.60 ± 0.34 54.37 ± 0.40 33.48 ± 0.30 52.76 ± 0.42 31.83 ± 0.12

# 有属性的结点分类

这是个为几个流行的图神经网络算法在有监督结点分类任务上构建的排行榜.

Rank Method Cora Citeseer Pubmed
1 Grand (Feng et al., NIPS'20) (opens new window) 84.8 ± 0.3 75.1 ± 0.3 82.4 ± 0.4
2 GCNII (Chen et al., ICML'20) (opens new window) 85.1 ± 0.3 71.3 ± 0.4 80.2 ± 0.3
3 DR-GAT (Zou et al., 2019) (opens new window) 83.6 ± 0.5 72.8 ± 0.8 79.1 ± 0.3
4 MVGRL (Hassani et al., KDD'20) (opens new window) 83.6 ± 0.2 73.0 ± 0.3 80.1 ± 0.7
5 APPNP (Klicpera et al., ICLR'19) (opens new window) 84.3 ± 0.8 72.0 ± 0.2 80.0 ± 0.2
6 Graph U-Net (Gao et al., 2019) (opens new window) 83.3 ± 0.3 71.2 ± 0.4 79.0 ± 0.7
7 GAT (Veličković et al., ICLR'18) (opens new window) 82.9 ± 0.8 71.0 ± 0.3 78.9 ± 0.3
8 GDC_GCN (Klicpera et al., NeurIPS'19) (opens new window) 82.5 ± 0.4 71.2 ± 0.3 79.8 ± 0.5
9 DropEdge(Rong et al., ICLR'20) (opens new window) 82.1 ± 0.5 72.1 ± 0.4 79.7 ± 0.4
10 GCN (Kipf et al., ICLR'17) (opens new window) 82.3 ± 0.3 71.4 ± 0.4 79.5 ± 0.2
11 DGI (Veličković et al., ICLR'19) (opens new window) 82.0 ± 0.2 71.2 ± 0.4 76.5 ± 0.6
12 JK-net (Xu et al., ICML'18) (opens new window) 81.8 ± 0.2 69.5 ± 0.4 77.7 ± 0.6
13 GraphSAGE (Hamilton et al., NeurIPS'17) (opens new window) 80.1 ± 0.2 66.2 ± 0.4 77.2 ± 0.7
14 GraphSAGE(unsup)(Hamilton et al., NeurIPS'17) (opens new window) 78.2 ± 0.9 65.8 ± 1.0 78.2 ± 0.7
15 Chebyshev (Defferrard et al., NeurIPS'16) (opens new window) 79.0 ± 1.0 69.8 ± 0.5 68.6 ± 1.0
16 MixHop (Abu-El-Haija et al., ICML'19) (opens new window) 81.9 ± 0.4 71.4 ± 0.8 80.8 ± 0.6
最后更新于: 2021/4/13 下午8:07:23