Name Disambiguation from link data in a collaboration graph using temporal and topological features

TL;DR

This paper introduces a privacy-preserving method for name disambiguation in collaboration graphs using only temporal and topological link data, avoiding external or biographical information.

Contribution

It proposes a novel approach leveraging only link data from collaboration networks for entity disambiguation, addressing privacy concerns and data availability issues.

Findings

Effective disambiguation on real-world networks

Avoids reliance on external or biographical data

Shows satisfactory performance in experiments

Abstract

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.