A Capsule Network-based Model for Learning Node Embeddings
Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning .
['[BOLD] Method (Micro-F1)', '[BOLD] POS  [ITALIC] γ=10%', '[BOLD] POS  [ITALIC] γ=50%', '[BOLD] POS  [ITALIC] γ=90%', '[BOLD] PPI  [ITALIC] γ=10%', '[BOLD] PPI  [ITALIC] γ=50%', '[BOLD] PPI  [ITALIC] γ=90%', 'BlogCatalog  [ITALIC] γ=10%', 'BlogCatalog  [ITALIC] γ=50%', 'BlogCatalog  [ITALIC] γ=90%']
PPI, POS and BlogCatalog. the Micro-F1 and Macro-F1 scores on test sets in the transductive setting. Especially, on POS, Caps2NE produces a new state-of-the-art Macro-F1 score for each of the three fraction values γ, the highest Micro-F1 score when γ=90% and the second highest Micro-F1 scores when γ∈{10%,50%}. Caps2NE obtains new highest F1 scores on PPI and BlogCatalog when γ=10% and γ=90%, respectively. On PPI, Caps2NE also achieves the highest Macro-F1 score when γ=50% and the second highest Micro-F1 score when γ=90%. On BlogCatalog, Caps2NE also achieves the second highest Macro-F1 scores when γ∈{10%,50%}.