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Table 1 Classification accuracies for labeled graph datasets

From: Sequence-to-sequence modeling for graph representation learning

Datasets MUTAG PTC Enzymes fig1 Nci1 Nci109  
# Graphs 188 344 600 1113 4110 4127  
EntropyWL1 2.72 4.03 5.45 5.54 4.21 4.22  
EntropyWL2 4.65 7.09 12.60 13.26 8.23 8.25  
ClusteringCoef 0 0.0025 0.453 0.51 0.003 0.003  
Avg. Nodes 17.9 25.5 32.6 39.1 29.8 29.6  
# Labels 7 19 2 3 37 38  
# Classes 2 2 6 2 2 2 Avg Rank
SPK (BK05) 85.2 ±2.4 58.2 ± 2.4 40.1 ± 1.5 75.1 ± 0.5 73.0 ± 0.2 73.0 ± 0.2 7.5
RWK (G+03) 83.7 ±1.5 57.8 ±1.3 24.2 ±1.6 74.2 ±0.4
GK (S+09) 81.7 ± 2.1 57.2 ± 1.4 26.6 ±0.9 71.7 ±0.5 62.3 ± 0.2 62.6 ± 0.1 8.5
WLSK (S+11) 80.7 ±3.0 57.0 ±2.0 53.1 ±1.1 72.9 ±0.5 80.1 ±0.5 80.2 ±0.3 8.5
node2vec (G+16) 82.01 ±1.0 55.60 ±1.4 19.42 ±2.3 70.76 ±1.2 61.91 ±0.3 61.53 ±0.9 9.5
DGK (YW15) 87.4 ±2.7 60.1 ±2.5 53.4 ±0.9 75.7 ±0.5 80.3 ±0.4 80.3 ±0.3 5.6
PSCN (N+16) 9 2 . 6 62.3 75.9 78.6
WL-OA (K+16) 86.0 ±1.7 63.6 ±1.5 59.9 ±1.1 76.4 ±0.4 86.1 ±0.2 86.3 ±0.2 3.3
GCN (KI+17) 86.3 ±2.1 63.28 ±3.9 56.6 ±3.5 75.9 ±2.8 81.1 ±1.6 80.7 ±1.8 5.0
graph2vec (N+17) 83.15 ±9.2 60.17 ±6.9 73.30 ±2.0 73.22 ±1.9 74.26 ±1.5
WL PM (NI+17) 87.77 ±0.8 61.41 ±0.8 55.55 ±0.5 86.40 ±0.2 85.34 ±0.2
LWL (M+17) 85.2 ±1.6 64.7 ±0.2 61.8 ±1.2 76.4 ±0.7 83.1 ±0.2 82.0 ±0.3 3.0
DGCNN (Z+18) 85.83 ±1.6 58.59 ±2.4 75.54 ±0.9 74.44 ±0.4
SGR (T+18) 86.97 33.67 73.83
S2S-N2N-PP 89.86 ±1.1 64.54 ±1.1 6 3 . 9 6 ± 0 . 6 76.51 ±0.5 83.72 ±0.4 83.64 ±0.3 2 . 5
supervised 89.91 ±1.5 6 5 . 7 2 ± 1 . 2 57.48 ±0.8 7 7 . 2 8 ± 0 . 9 8 6 . 6 5 ± 0 . 6 8 6 . 6 1 ± 0 . 5 1 . 5