TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Table 3. Ablation analysis of model architectures on MAG-CS dataset. We assign an index to each model variant (shown in the first column). All models are run three times with their averaged scores reported.
['Ind', 'Graph Propagate', 'Graph Readout', 'Matching', 'MR', 'Hit@1', 'Hit@3', 'MRR']
2. Ablation Analysis of Model Architectures. TaxoExpan contains three key components: a graph propagation module, a graph readout module, and a matching model. Here, we study how different choices of these components affect the performance of TaxoExpan. 3. Ablation Analysis of Training Schemes. In this subsection, we evaluate the effectiveness of our proposed training scheme. In this study, we first group a set of positive and negative ⟨query,anchor⟩ pairs into one single training instance (c.f. Sect. An alternative is to treat these pairs as different instances and train the model using standard binary cross entropy (BCE) loss. Under this training scheme, we formulate our problem as a binary classification task. Our proposed training scheme with InfoNCE loss is overall much better, it beats the BCE loss scheme on 14 out of total 16 cases. One reason is that BCE loss is very sensitive to the noises in the generated self-supervision data while InfoNCE loss is more robust to such label noise. Furthermore, we find that LBM matching can benefit more from our training scheme with InfoNCE loss – with larger margin on all 8 cases, compared with the simple MLP matching.