Can Author Collaboration Reveal Impact? The Case of h-index

Abstract

Scientific impact has been the center of extended debate regarding its accuracy and reliability. From hiring committees in academic institutions to governmental agencies that distribute funding, an author's scientific success as measured by ℎ-index is a vital point to their career. The objective of this work is to investigate whether the collaboration patterns of an author are good predictors of the author's future ℎ-index. Although not directly related to each other, a more intense collaboration can result into increased productivity which can potentially have an impact on the author's future ℎ-index. In this paper, we capitalize on recent advances in graph neural networks and we examine the possibility of predicting the ℎ-index relying solely on the author's collaboration and the textual content of a subset of their papers. We perform our experiments on a large-scale network consisting of more than 1 million authors that have published papers in computer science venues and more than 37 million edges. The task is a six-months-ahead forecast, i. e., what the ℎ-index of each author will be after six months. Our experiments indicate that there is indeed some relationship between the future ℎ-index of an author and their structural role in the co-authorship network. Furthermore, we found that the proposed method outperforms standard machine learning techniques based on simple graph metrics along with node representations learnt from the textual content of the author's papers.

How can I improve my scientific impact? The most influential factors in predicting the h-index

“…The performances of proposed models indicate that still more features that don't depend on the history of publications and citations are required to forecast the feature h-index of young researchers. For example, the textual content of papers examined by [13] and topic authority by [41] can be applied together with introduced features in this study to improve the performance.…”

“…To identify the future scientific impact, some studies focus on predicting the citations' count for a specific paper [8,9,10,11,12], others tried to predict the impact at the author level with the h-index [5,6,7,13]. Among all models and methods presented in these studies to predict the h-index, those that took the number of prior publications, received citations, or the current h-index (prior impact-based features) into consideration achieved the highest performance.…”

“…Their results showed the significance of co-authors' productivity via collaborating with many authors and their impact on predicting the h-index. [13] extracted two types of features, papers' textual content and graph features (related to collaboration patterns), and found that graph features alone are stronger predictors. [16] studied the contribution of a publication to the author's h-index and found that topical authority and publication venues are the most predictive features in the absence of citation-related features of prior publications.…”

“…Many studies employed Machine learning regression and classification approaches to predict the scientific impact of publications and researchers [6,13,9,11,10,7]. Most common methods in these studies were regression models such as Support Vector Regression (SVR), Gradient boosted regression trees (GBRT) or Gradient Boosting (GB), Gradient-boosting decision tree (GBDT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-nearest neighbour (KNN), and Neural networks (NN).…”