# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 2 Related Work  
The problem of NDA differs from named-entity disambiguation (NED; also known as named entity linking), a natural language processing (NLP) task where the purpose is to identify which real-life entity from a list a named-entity in a text refers to. For example, in the ArnetMiner dataset [7] '[PERSON]' corresponds to more than 10+ authors. The Open Researcher and Contributor ID (ORCID) [8] was introduced to solve the author name ambiguity problem, and most NED methods rely on ORCID for labeling datasets. NED in this context aims to match the author names to unique (unambiguous) author identifiers [7; 9; 10; 11]. In [7], they exploit hidden Markov random fields in a unified probabilistic framework to model node and edge features. On the other hand, [PERSON] et al. [12] designed a comprehensive framework to tackle name disambiguation, using complex feature engineering approach. By constructing paper networks, using the information sharing between two papers to build a supervised model for assigning the weights of the edges of the paper network. If two nodes in the network are connected, they are more likely to be authored by the same person. Recent approaches are increasingly relying on more complex data, [PERSON] et al. [13] used heterogeneous bibliographic networks representation learning, by employing relational and paper-related textual features, to obtain the embeddings of multiple types of nodes, while using meta-path based proximity measures to evaluate the neighboring and structural similarities of node embedding in the heterogeneous graphs. The work of [PERSON] et al. [9] focusing on preserving privacy using solely the link information in a graph, employs network embedding as an intermediate step to perform NED, but they rely on other networks (person-document and document-document) in addition to person-person network to perform the task. Although NDA could be used to assist in NED tasks, NED typically strongly relies on the text, e.g., by characterizing the context in which the named entity occurs (e.g., papertopic) [14]. Similarly, [PERSON] et al. [15] proposes a name disambiguation model based on representation learning employing attributes and network connections, by first encoding the attributes of each paper using variational graph auto-encoder, then computing a similarity metric from the relationship of these attributes, and then using graph embedding to leverage the author relationships, heavily relying on NLP. In NDA, in contrast, no natural language is considered, and the goal is to rely on just the network's connectivity in order to identify which nodes may correspond to multiple distinct entities. Moreover, NDA does not assume the availability of a list of known unambiguous entity identifiers, such that an important part of the challenge is to identify which nodes are ambiguous in the first place. This offers a more privacy-friendly advantage and extends the application towards more datasets where access to additional information is restricted or not possible. The research by [PERSON] et al. [16], and [PERSON] et al. [17] is most closely related to ours. These papers also only use topological information of the network for NDA. Yet, Ref. [16] also require timestamps for the edges, while [17] require a training set of nodes labeled as ambiguous and non-ambiguous. Moreover, even though the method proposed by [16] is reportedly orders of magnitude faster than the one proposed by [17], it remains computationally substantially more demanding than FONDUE (e.g., [16] evaluate their method on networks with just 150 entities). Other recent work using NE for NED [9; 18; 19; 20] is only related indirectly as they rely on additional information besides the topology of the network. The literature on NDD is scarce, as the problem is not well-defined. Conceptually, it is similar to that of named entity linking (NEL) [11; 21] problem which aims to link instances of named entities in a text such as a newspaper, articles to the corresponding entities, often in knowledge bases (KB).