NDLI: On the Formation of Circles in Co-authorship Networks

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On the Formation of Circles in Co-authorship Networks

Content Provider	ACM Digital Library
Author	Goyal, Pawan Chakraborty, Tanmoy Patranabis, Sikhar Mukherjee, Animesh
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 Prec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.
Starting Page	109
Ending Page	118
Page Count	10
File Format	PDF MP4
ISBN	9781450336642
DOI	10.1145/2783258.2783292
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Circles Prediction Ego networks
Content Type	Audio Text
Resource Type	Article

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