On the Formation of Circles in Co-authorship Networks

TL;DR

This paper presents an unsupervised method for automatically detecting densely knit researcher communities, called circles, in co-authorship networks, improving community detection and collaboration prediction performance.

Contribution

It introduces a novel unsupervised approach combining node features and similarity measures to identify circles in large co-authorship networks, outperforming existing methods.

Findings

13.33% improvement in overlapping modularity over state-of-the-art methods

Circle information enhances collaboration prediction by up to 15.25% in P@20

Model applied to a network of over 800,000 authors.

Abstract

The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of "circles", which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks. In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -- two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AUC (Area under the ROC) and P rec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.