On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks

TL;DR

This paper explores how hierarchical topological features and pattern recognition can improve the disambiguation of author names in collaborative networks, especially by considering longer-range connections and multiple data sources.

Contribution

It demonstrates that using third-level hierarchical connections and network topology enhances the ability to distinguish homonymous authors in collaborative networks.

Findings

Longer-distance connections improve disambiguation accuracy.

Pattern recognition strategies effectively utilize network topology.

Combining topology features with long-range connections further enhances results.

Abstract

Many features of complex systems can now be unveiled by applying statistical physics methods to treat them as social networks. The power of the analysis may be limited, however, by the presence of ambiguity in names, e.g., caused by homonymy in collaborative networks. In this paper we show that the ability to distinguish between homonymous authors is enhanced when longer-distance connections are considered, rather than looking at only the immediate neighbors of a node in the collaborative network. Optimized results were obtained upon using the 3rd hierarchy in connections. Furthermore, reasonable distinction among authors could also be achieved upon using pattern recognition strategies for the data generated from the topology of the collaborative network. These results were obtained with a network from papers in the arXiv repository, into which homonymy was deliberately introduced to test the methods with a controlled, reliable dataset. In all cases, several methods of supervised and unsupervised machine learning were used, leading to the same overall results. The suitability of using deeper hierarchies and network topology was confirmed with a real database of movie actors, with the additional finding that the distinguishing ability can be further enhanced by combining topology features and long-range connections in the collaborative network.