# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation
#### 4.2.4 Quantitative Evaluation of Nodes Splitting  
Following the identification of the ambiguous nodes, in this section, we focus on the task of node splitting, and answering \(\mathbf{Q}_{3}\) of how well does FONDUE-NDA perform when it comes to partitioning the set of edges into two separate ambiguous nodes. Simply put, given an ambiguous node \(v_{i}\), we refer to node splitting the process of replacing this particular node with two different nodes \(v^{\prime}_{i},v^{\prime\prime}_{i}\) and re-assigning the edges of \(v_{i}\), such that \(\Gamma(v^{\prime}_{i})\cup\Gamma(v^{\prime\prime}_{i})=\Gamma(v_{i})\).  
**Baselines.** For the node splitting task, the three baselines previously discussed in Section 4.2.2 are not immediately applicable. However, we adopt the Markov clustering (**MCL**) approach utilized in normalized cut measure for splitting. Namely, a splitting is given by the MCL clustering on the ego network of an ambiguous node, with the node itself removed.  
**Evaluation Metric.** Given a list of ambiguous nodes, we evaluate the splitting given by FONDUE-NDA and MCL against the ground truth (node splitting according to the original network). This is quantified by computing the adjusted Rand index (ARI) score between FONDUE-NDA and the ground truth, as well as between MCL and the ground truth. ARI score is a similarity measure between two clusterings. ARI ranges between \(-1\) and \(1\), the higher the score the better the alignment between the two compared clusterings.  
**Pipeline.** First we compute the ground truth. Then, for each ambiguous node, we evaluate the quality (based on ARI) of the split from FONDUE-NDA and MCL compared to the original partition. We repeat the experiments for three different contraction ratios \(r\in\{0.001,0.01,0.1\}\) for each dataset. For each ratio, the experiment is repeated 3 times with different seeds.  
**Results.** Despite outperforming NC in ambiguous node identification, FONDUE-NDA seems to underperform compared to MCL on nearly every dataset (Table 6). This shows that for node splitting, FONDUE-NDA is not adequately optimized for such task. Many factors contribute to the poor performance, mainly the quality of the embeddings which would lead to a poor performance for the objective function for edge assignment. Nonetheless, given the modular approach for our method, and for a complete framework for node identification and node splitting, we recommend using FONDUE-NDA for former task and MCL for the latter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NC Rank** & **Ambiguous** & **FONDUE-NDA Rank** & **Ambiguous** \\ \hline [PERSON]\_[PERSON] & 0 & [PERSON]. [PERSON] * & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] * & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 0 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] * & 1 \\ [PERSON]\_[PERSON] & 0 & [PERSON]\_[PERSON] & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The top 10 ranked nodes of FONDUE-NDA and NC, against the ground truth (1 if the node is ambiguous and 0 otherwise). FONDUE-NDA correctly classifies 9 out of the 10 nodes as ambiguous. Names with * are highly ambiguous, referring to more than 2 Orcid IDs.