Social Network Analysis for Predicting Emerging Researchers

Abstract

Finding rising stars in academia early in their careers has many implications when hiring new faculty, applying for promotion, and/or requesting grants. Typically, the impact and productivity of a researcher are assessed by a popular measurement called the h-index that grows linearly with the academic age of a researcher. Therefore, h-indices of researchers in the early stages of their careers are almost uniformly low, making it difficult to identify those who will, in future, emerge as influential leaders in their field. To overcome this problem, we make use of social network analysis to identify young researchers most likely to become successful as measured by their h-index. We assume that the co-authorship graph reveals a great deal of information about the potential of young researchers. We built a social network of 62,886 researchers using the data available in CiteSeerx. We then designed and trained a linear SVM classifier to identify emerging authors based on their personal attributes and/or their networks of co-authors. We evaluated our classifier's ability to predict the future research impact of a set of 26,170 young researchers, those with an h-index of less than or equal to two in 2005. By examining their actual impact six years later, we demonstrate that the success of young researchers can be predicted more accurately based on their professional network than their established track records.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 17 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.