# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation
#### 4.2.2 Quantitative Evaluation of Node Identification
** are illustrated in Figure 3 and shown in detail in Table 3 focusing on NDCG mainly for being a better measure for assessing the ranking performance of each method. FONDUE-NDA outperforms the state-of-the-art method, as well as non-trivial baselines in terms of NDCG in most datasets. It is also more robust with the variation of the size of the network, and the fraction of the ambiguous nodes in the graph. NC seems to struggle to identify ambiguous nodes for smaller networks (Table 2). Additionally, as we tested against multiple network settings, with randomly uniform contraction (randomly selecting a node-pair and merging them together), or a conditional contraction (selecting a node pair that do not share common neighbors to mimic realistically collaboration networks), we did not observe any significant changes in the results. \begin{table}
\begin{tabular}{r r r r r r r r r r r r r} \hline \hline \multicolumn{2}{c}{**Ambiguity**} & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**1\%**} & \multicolumn{4}{c}{**0.1\%**} \\ \multicolumn{2}{c}{**Rate**} & \multicolumn{4}{c}{**10\%**} & \multicolumn{4}{c}{**1\%**} & \multicolumn{4}{c}{**0.2\%**} \\ \hline  & **Method** & **FONDUE-NDA** & **NC** & **CC** & **Degree** & **FONDUE-NDA** & **NC** & **CC** & **Degree** & **FONDUE-NDA** & **NC** & **CC** & **Degree** \\ \hline \multirow{4}{*}{**b-sc**} & **fb-pc** & 0.954 & 0.962 & 0.768 & 0.776 & 0.767 & 0.875 & 0.569 & 0.423 & 0.679 & 0.535 & 0.272 & 0.199 \\  & **fb-pp** & 0.899 & 0.825 & 0.821 & 0.804 & 0.649 & 0.532 & 0.528 & 0.511 & 0.374 & 0.268 & 0.268 & 0.253 \\  & **email** & 0.783 & 0.661 & 0.619 & 0.704 & 0.529 & 0.305 & 0.264 & 0.310 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **student** & 0.778 & 0.664 & 0.568 & 0.652 & 0.396 & 0.328 & 0.235 & 0.257 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **lesmis** & 0.906 & 0.570 & 0.499 & 0.622 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\  & **polbooks** & 0.972 & 0.604 & 0.534 & 0.698 & 1.000 & 0.310 & 0.267 & 0.318 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **ppi** & 0.759 & 0.670 & 0.724 & 0.741 & 0.420 & 0.353 & 0.381 & 0.387 & 0.194 & 0.138 & 0.147 & 0.151 \\  & **netscience** & 0.886 & 0.784 & 0.731 & 0.721 & 0.508 & 0.378 & 0.323 & 0.288 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **GrQc** & 0.857 & 0.805 & 0.796 & 0.768 & 0.603 & 0.447 & 0.437 & 0.415 & 0.249 & 0.195 & 0.184 & 0.168 \\  & **CondMat** & 0.864 & 0.855 & 0.843 & 0.816 & 0.601 & 0.553 & 0.543 & 0.520 & 0.367 & 0.278 & 0.269 & 0.255 \\  & **HepTh** & 0.860 & 0.798 & 0.823 & 0.796 & 0.582 & 0.466 & 0.494 & 0.470 & 0.325 & 0.201 & 0.224 & 0.208 \\  & **cm05** & 0.884 & 0.873 & 0.859 & 0.827 & 0.627 & 0.590 & 0.582 & 0.545 & 0.471 & 0.312 & 0.307 & 0.288 \\  & **cm03** & 0.888 & 0.869 & 0.852 & 0.823 & 0.635 & 0.577 & 0.562 & 0.534 & 0.335 & 0.297 & 0.281 & 0.272 \\ \hline \multirow{4}{*}{**c**} & **fb-sc** & 0.953 & 0.989 & 0.768 & 0.764 & 0.730 & 0.933 & 0.951 & 0.418 & 0.399 & 0.665 & 0.321 & 0.172 \\  & **fb-pp** & 0.895 & 0.826 & 0.820 & 0.801 & 0.650 & 0.532 & 0.529 & 0.510 & 0.389 & 0.266 & 0.267 & 0.253 \\  & **email** & 0.676 & 0.696 & 0.625 & 0.604 & 0.303 & 0.319 & 0.288 & 0.256 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **student** & 0.659 & 0.726 & 0.531 & 0.587 & 0.368 & 0.447 & 0.201 & 0.229 & \(-\) & \(-\) & \(-\) & \(-\) \\  & **lesmis** & 0.755 & 0.591 & 0.498 & 0.486 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\  & **polbooks** & 0.981 & 0.620 & 0.544 & 0.696 & 1.000 & 0.268 & 0.420 & 0.363 & \(-\) & \(-\) & \(-\) \\  & **pp