A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors' future h-indices based on their current scientific impact. Second, we examine the factors that drive papers-either previously or newly published-to increase their authors' predicted future h-indices. By leveraging relevant factors, we can predict an author's h-index in five years with an R ₂ value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an F ₁ score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants”.

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."

Predicting the distributions of species is central to a variety of applications in ecology and conservation biology. With increasing interest in using electronic occurrence records, many modeling techniques have been developed to utilize this data and compute the potential distribution of species as a proxy for actual observations. As the actual observations are typically overwhelmed by non-occurrences, we approach the modeling of species' distributions with a focus on the problem of class imbalance. Our analysis includes the evaluation of several machine learning methods that have been shown to address the problems of class imbalance, but which have rarely or never been applied to the domain of species distribution modeling. Evaluation of these methods includes the use of the area under the precision-recall curve (AUPR), which can supplement other metrics to provide a more informative assessment of model utility under conditions of class imbalance. Our analysis concludes that emphasizing techniques that specifically address the problem of class imbalance can provide AUROC and AUPR results competitive with traditional species distribution models.

A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.