# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation
#### 4.2.3 Qualitative Evaluation of Nodes Identification  
Now that we have demonstrated that FONDUE-NDA outperforms current state-of-the-art-methods for identifying ambiguous nodes, in this section we investigate \(\mathbf{Q}_{3}\), the quality of the results produced by FONDUE-NDA and how do they compare to other competing methods. Additionally, we investigate the question, whether nodes with a higher ambiguity number (nodes that map to a larger number of entities) are more easily identified as ambiguous compared to nodes with lower ambiguity number (that map to a lower number of entities). To do so, semi-synthetic datasets do not constitute valid sources, and we need to rely on real-world dataset to assess the quality of the ground truth of the ambiguous nodes. We collected data from the National Center for Biotechnology Information that provides the PubMed datasets, that comprises citations for biomedical literature from MEDLINE, life science journals, and online books, that may include links to full-text content from PubMed Central and publisher websites. Snippets of those data are released periodically and can be used to build author-author collaboration networks. Us  
Figure 3: Bar plots for the different metrics (higher is better) for each dataset listed in Table 3, for each of the 4 measures, FONDUE-NDA, Degree, NC, CC, for network with 10% uniformly contracted nodes. More detailed results are listed in Appendix B.  
ing Orcid ID, a persistent digital identifier, usually provided in the metadata of the datasets to distinguish ambiguous nodes, we can build reliable ground truth for our qualitative experiment and investigate the top ranked nodes by FONDUE-NDA, and other baselines. **Datasets. ** PubMed datasets are publicly available, and updated regularly. Few data snippets are selected to extract author information and build an author-author collaboration network based on the metadata included in the datasets. The biggest connected component of the network is then selected. To assess the ambiguity of the network, we also extract the Orcid ID, if available, for each author in the network. If more than Orcid iD is associated with one author, the author-name is labeled as ambiguous. Note that not all authors have their Orcid ID listed in the datasets, which might cause a possible degradation in the quality of the labeled datasets, as some author-names might be ambiguous, but due to their Orcid ID not being recorded, they will not be accounted for in the labels, thus affecting the final results. **Metrics. ** Similarly to Section 4.2.2, we first take the binary classification approach, where we consider the top 31 ranked nodes to be classified as ambiguous (as the network contains 31 ambiguous nodes). The same goes for NC. We use the same metrics from Section 4.2.2. Additionally, to assess the quality of the ranked nodes, we utilize the number of true positives (TP) (nodes that are correctly identified as ambiguous) and false positive (FP) (nodes that are incorrectly identified as ambiguous). We also consider NDCG for graded relevance. **Results. ** After building the PubMed collaboration network, that contains 2122 nodes with 31 ambiguous nodes (6 of which are names that refer to more than 2 authors), it is embedded using CNE, and we compute the score measure of FONDUE-NDA (Equation (7)), as well as the baseline measures for each node. As explained earlier in Section 4.2.2, FONDUE-NDA ranks the nodes by ambiguity score. For comparison, we use the work of [16] as a baseline for qualitative comparison. Table 4 shows the performance of FONDUE-NDA against NC, for AUC, TP, FP, and F1-score. FONDUE-NDA clearly outperforms NC for binary classification of ambiguous nodes. This result is also highlighted when inspecting further the results in Table 5, that list the top 10 classified nodes by each method, with FONDUE-NDA successfully classifying 90% of the author names.