A Capsule Network-based Model for Learning Node Embeddings
Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models.
['[BOLD] Transductive Un-sup.', '[BOLD] Transductive BoW', '[BOLD] Cora 58.63', '[BOLD] Citeseer 58.07', '[BOLD] Pubmed 70.49']
Cora, Citeseer and Pubmed. BoW is evaluated by directly using the pre-computed bag-of-words feature vectors for learning the classifier, thus its performance is the same for both settings. DeepWalk+BoW concatenates the learned embedding of a node from DeepWalk with the pre-computed BoW feature vector of the node. Inductive setting: As previously discussed in the last paragraph in the “The proposed Caps2NE” section, we re-emphasize that our unsupervised Caps2NE model notably outperforms the supervised models GCN and GAT for this inductive setting. In particular, Caps2NE achieves 4+% absolute higher accuracies than both GCN and GAT on the three datasets, clearly showing the effectiveness of Caps2NE to infer embeddings for unseen nodes.