Efficacy of a giant component in co-authorship networks Evidence from a Southeast Asian dataset in economics

Purpose - The purpose of this paper is to investigate whether a sparse and relatively small giant component (GC) will capture highly productive authors. Design/methodology/approach - The author used a geographically dispersed data set involving authors in the field of economics in ten countries in Southeast Asia and applied social network analysis methods to investigate the structure and dynamics of GCs. Findings - Results reveal that a GC, characterized by both low density and small size, can still capture a significant percentage (68 per cent of the top 25) of the most productive authors. There seems to be a topological backing for this occurrence. The number of direct connections (or "degree") in the GC was correlated with research productivity, such that high-degree authors were almost twice as productive as low-degree authors. It is probable that productive authors having higher than average degrees may be the cause of the formation of the GC. The author hypothesize that irrespective of its size or sparseness, GCs in co-authorship networks may still represent the seat of main intellectual activity in the network. Originality/value - This is one of the first studies to quantitatively analyse the ability of a co-authorship-based less-prominent GC to capture prominent authors.