# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Deduplication  
#### 4.3.1 Quantitative Evaluation of Node Deduplication  
In this section we describe the details of the experimental setup used to tackle the NDD problem. Referring to the data listed in Table 1, we performed our experiments on three semi-synthetic datasets. We also used the PubMed dataset with metadata containing Orcid ID to also build a duplicate network with ground truth derived from the author names with common Orcid ID. **Evaluation Pipeline. ** As we indicated in Section 4.1, we perform post-processing on the graph data to conduct controlled experiments for comparison with other baselines. For the semi-synthetic datasets (Table 2), to simulate the data corruption process caused by the NDD problem, we perform node splitting on different datasets. For a given graph \(G=(V,E)\) with \(n=|V|\), we compute the embedding cost function of \(G\). We then randomly choose one node \(i\) to split into two \(i^{\prime}\) and \(i^{\prime\prime}\), i.e., adding a new node to the graph, which results in a corrupted (duplicate) graph \(\hat{\mathcal{G}}=(\hat{V},\hat{E})\) with \(n_{d}=|\hat{V}|=n+1\). We then randomly choose one node-pair from \(\hat{V}\times\hat{V}\), and perform node contraction, by merging these 2 nodes such that we end up with 1 node containing all the edges of the previous 2, which results in contracted graph \(\hat{\mathcal{G}}_{c}=(\hat{V}_{c},\hat{E}_{c})\) with \(|\hat{V}_{c}|=n\). We then compute the embedding cost function of \(\mathcal{G}_{c}^{\prime}\). We repeat the process by choosing a different random node-pair from \(V_{c}\) and computing the resulting embedding cost function, 99 times. Lastly, we compute the embedding cost function after merging the duplicate node pair \(\{i^{\prime},i^{\prime\prime}\}\). We compare the value of the objective cost function with that of graph \(G\), and display the ranking in Table 8. Although the process of data corruption seems simple, there \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**10\%**} & \multicolumn{2}{c}{**1\%**} & \multicolumn{2}{c}{**0.1\%**} \\ \hline  & \multicolumn{2}{c}{**FONDUE-**} & \multicolumn{2}{c}{**NDA**} & \multicolumn{2}{c}{**CC**} & \multicolumn{2}{c}{**FONDUE-**} & \multicolumn{2}{c}{**NC**} & \multicolumn{2}{c}{**CC**} & \multicolumn{2}{c}{**FONDUE-**} & \multicolumn{2}{c}{**NC**} & \multicolumn{2}{c}{**CC**} \\ \hline
**fb-sc** & 10.99 & 94.72 & 52.92 & 5.16 & 90.43 & 51.62 & 7.66 & 90.99 & 51.63 \\
**fb-pp** & 15.03 & 161.60 & 46.83 & 18.02 & 185.64 & 46.48 & 29.65 & 190.25 & 46.06 \\
**email** & 0.81 & 10.18 & 4.77 & 0.84 & 10.26 & 4.62 & – & – & – \\
**student** & 0.35 & 2.88 & 0.69 & 0.37 & 4.45 & 0.59 & – & – & – \\
**lesmis** & 0.14 & 0.21 & 0.03 & – & – & – & – & – & – \\
**polbooks** & 0.04 & 0.34 & 0.05 & 0.05 & 0.36 & 0.05 & – & – & – \\
**pipi** & 4.09 & 49.23 & 15.64 & 4.17 & 50.89 & 15.03 & 3.53 & 50.73 & 14.95 \\
**nstence** & 0.12 & 0.89 & 0.08 & 0.13 & 0.92 & 0.09 & 0.13 & 0.91 & 0.09 \\
**G4C2** & 1.53 & 13.07 & 2.06 & 1.66 & 13.36 & 2.07 & 1.69 & 13.29 & 2.04 \\
**CondMat** & 11.41 & 82.63 & 13.15 & 12.36 & 86.42 & 13.11 & 13.76 & 88.26 & 12.75 \\
**HepTh** & 3.47 & 28.04 & 2.85 & 3.65 & 27.22 & 2.81 & 4.00 & 27.68 & 2.77 \\
**cm05** & 23.04 & 143.23 & 30.02 & 24.69 & 144.98 & 25.12 & 26.01 & 137.92 & 25.51 \\
**cm03** & 16.10 & 116.26 & 17.82 & 20.33 & 137.57 & 17.12 & 18.96 & 124.25 & 17.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Execution time (in seconds) comparison table for the different datasets averaged over 10 different experiments. The embedding time of the networks was not taken into account for the computation of the runtime results. Figure 4: Bar plots for the runtime performance in seconds (lower is better) for each dataset listed in Table 7, for each of the 3 measures, FONDUE-NDA, NC, CC for different percentage of contracted nodes, 0.1%, 1%, and 10%, respectively. are many parameter variables at hand that can affect it.