Name Disambiguation in Anonymized Graphs using Network Embedding

Abstract

In real-world, our DNA is unique but many people share names.is phenomenon o en causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper a ribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using a ributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly be er than the existing name disambiguation methods working in a similar se ing.

Strong Baselines for Author Name Disambiguation with and Without Neural Networks

“…Since there is no implementation details, we speculate that it might be because the merging rules are too loose. By incorporating both rules and neural networks to model co-authorships, affiliations and titles explicitly, our PNP model outperforms all baselines in terms of F1score (+3.54% over Zhang et al [25], +11.75% over Zhang and Al Hasan [24], +11.23% over Louppe et al [13] and +39.74% over Fan et al [3] relatively). In the bottom half part of Table 1, some incremental results of our method are presented.…”

“…-Louppe et al [13]: It trains a pairwise distance function based on carefully designed similarity features, and uses semi-supervised Hierarchical Agglomerative Clustering (HAC) algorithm to determine clusters. -Zhang and Al Hasan [24]: It constructs graphs for each author name based on co-author and document similarity. Embeddings are learned for each name and the final results are also obtained by HAC.…”

A scalable attribute-aware network embedding system

“…With the development of unsupervised feature learning techniques [3], deep learning methods proved successful in natural language processing tasks through neural language models [10,33,36]. These models have been used to capture the semantic and syntactic structures of human language [8], and even logical analogies [20], by embedding words as vectors.…”

Multi-level network embedding with boosted low-rank matrix approximation

“…Additionally, as the node embedding representations are often learned in a task-agnostic fashion, they are generalizable to a number of downstream learning tasks such as node classification [33], community detection [44], link prediction [15], and visualization [37]. On top of that, it also has broader impacts in advancing many real-world applications, ranging from recommendation [45], polypharmacy side effects prediction [53] to name disambiguation [49]. The basic idea of network embedding is to represent each node by a lowdimensional vector in which the relativity information among nodes in the original network is maximally transcribed.…”