# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
###### Abstract  
Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for many purposes, but the efficacy of downstream tasks is often hampered by data quality issues related to their construction. In many constructed networks, ambiguity may arise when a node corresponds to multiple concepts. Similarly, a single entity can be mistakenly represented by several different nodes. In this paper, we formalize both the node disambiguation (NDA) and node deduplication (NDD) tasks to resolve these data quality issues. We then introduce FONDUE, a framework for utilizing network embedding methods for data-driven disambiguation and deduplication of nodes. Given an undirected and unweighted network, FONDUE-NDA identifies nodes that appear to correspond to multiple entities for subsequent splitting and suggests how to split them (node disambiguation), whereas FONDUE-NDD identifies nodes that appear to correspond to same entity for merging (node deduplication), using only the network topology. From controlled experiments on benchmark networks, we find that FONDUE-NDA is substantially and consistently more accurate with lower computational cost in identifying ambiguous nodes, and that FONDUE-NDD is a competitive alternative for node deduplication, when compared to state-of-the-art alternatives. node disambiguation; node deduplication; node linking; entity linking; network embeddings; representation learning +
Footnote †: 2021  
a dramatic effect on the implicit representation of other nodes, by dramatically changing distances on the network. Similarly, mistakenly representing distinct real-life entities by the same node in the network may dramatically alter its structural properties, by increasing the degree of the node and by merging the possibly quite distinct neighborhoods of these entities into one. Conversely, representing the same real-life entity by multiple nodes can also negatively affect the topology of the graph, possibly even splitting apart communities. Although identifying missing edges and, conversely, identifying incorrect edges, can be tackled adequately using link prediction methods, prior work has neglected the other task: identifying and appropriately splitting nodes that are ambiguous--i.e., nodes that correspond to more than one real-life entity. We will refer to this task as _node disambiguation (NDA)_. A converse and equally important problem is the problem of identifying multiple nodes corresponding to the same real-life entity,a problem we will refer to as _node deduplication (NDD)_. This paper proposes a unified and principled framework to both NDA and NDD problems, called framework for node disambiguation and deduplication using network embeddings (FONDUE). FONDUE is inspired by the empirical observation that real (natural) networks tend to be easier to embed than artificially generated (unnatural) networks, and rests on the associated hypothesis that the existence of ambiguous or duplicate nodes makes a network less natural. Although most of the existing methods tackling NDA and NDD make use of additional information (e.g., node attributes, descriptions, or labels) for identifying and processing these problematic nodes, FONDUE adopts a more widely applicable approach that relies solely on topological information. Although exploiting additional information may of course increase the accuracy on those tasks, we argue that a method that does not require such information offers unique advantages, e.g., when data availability is scarce, or when building an extensive dataset on top of the graph data, is not feasible for practical reasons. Additionally, this approach fits the privacy by design framework, as it eliminates the need to incorporate more sensitive data.