# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 5 Conclusions  
In this paper, we formalized both the node deduplication problem and the node disambiguation problem as inverse problems. We presented FONDUE as a novel method that exploits the empirical fact that naturally occurring networks can be embedded well using state-of-the-art network embedding methods, such that the embedding quality of the network after node disambiguation or node deduplication can be used as an inductive bias.  
For node deduplication, we showed that FONDUE-NDD, using only the topological properties of a graph, can help identify nodes that are duplicate, with experiments on four different datasets successfully demonstrating the viability of the method. Despite it not  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Dataset** & **Execution Time (Seconds)** \\ \hline
**lesmis** & 609.34 \\
**polbooks** & 712.29 \\
**netscience** & 1198.63 \\
**pubmed** & 3474.82 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Runtime for FONDUE-NDD in seconds, for 100 iterations (contracting each time a different random node pair and computing its embeddings).  
being an end-to-end solution, it can facilitate filtering out the best candidate nodes that are duplicates.  
For tackling node disambiguation, FONDUE-NDA decomposes this task into two sub-tasks: identifying ambiguous nodes, and determining how to optimally split them. Using an extensive experimental pipeline, we empirically demonstrated that FONDUE-NDA outperforms the state-of-the-art when it comes to the accuracy of identifying ambiguous nodes, by a substantial margin and uniformly across a wide range of benchmark datasets of varying size, proportion of ambiguous nodes, and domain, while keeping the computational cost lower than that of the best baseline method, by nearly one order of magnitude.  
On the other hand, the boost in ambiguous node identification accuracy was not observed for the node splitting task, where FONDUE-NDA underperformed compared to the competing baseline, Markov clustering. Thus, we suggested a combination of FONDUE for node identification, and Markov clustering on the ego-networks of ambiguous nodes for node splitting, as the most accurate approach to address the full node disambiguation problem.  
Conceptualization, B.K. and T.D.B.; methodology, A.M., B.K. and T.D.B.; software, A.M. and B.K.; validation, A.M., B.K., J.L. and T.D.B.; formal analysis, A.M. and B.K.; investigation, A.M. and B.K.; resources, J.L. and T.D.B.; data curation, A.M. and B.K.; writing--original draft preparation, A.M., B.K., J.L. and T.D.B.; writing--review and editing, A.M., B.K., J.L. and T.D.B.; visualization, A.M. and B.K.; supervision, B.K., J.L. and T.D.B.; project administration, B.K.; funding acquisition, J.L. and T.D.B. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) (ERC Grant Agreement no. 615517), and under the European Union's Horizon 2020 research and innovation programme (ERC Grant Agreement No. 963924), from the Flemish Government under the \"Onderzoeksprogramma Artificiale Intelligentie (AI) Vlaanderen\" programme, and from the FWO (project no. G091017N, G0F9816N, 3G042220).  
All data used in this study are publicly available, from other sources. See Section 3.1 and Table 1 for sources.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.