Evaluating researchers' scientific productivity usually relies on bibliometry only, which may not be always fair. Here, we take a step forward on analyzing such data by exploring the strength of co-authorship ties in social networks. Specifically, we build co-authorship social networks by extracting the datasets of three research areas sociology, medicine and computer science from a real digital library and analyze how topological properties relate to the strength of ties. Our results show that different topological properties explain variations in the strength of co-authorship ties, depending on the research area. Also, we show that neighborhood overlap can be applied to scientific productivity evaluation and analysis beyond bibliometry.
['Michele A. Brandão', 'Mirella M. Moro']
Analyzing the Strength of Co-authorship Ties with Neighborhood Overlap