Leveraging Citation Networks to Visualize Scholarly Influence Over Time

Assessing the influence of a scholar's work is an important task for funding organizations, academic departments, and researchers. Common methods, such as measures of citation counts, can ignore much of the nuance and multidimensionality of scholarly influence. We present an approach for generating dynamic visualizations of scholars' careers. This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields. We developed our design in collaboration with one funding organization---the Pew Biomedical Scholars program---but the methods are generalizable to visualizations of scholarly influence. We applied the design method to the Microsoft Academic Graph, which includes more than 120 million publications. We validate our abstractions throughout the process through collaboration with the Pew Biomedical Scholars program officers and summative evaluations with their scholars

Understanding the Elephant: The Discourse Approach to Boundary Identification and Corpus Construction for Theory Review Articles

The goal of a review article is to present the current state of knowledge in a research area. Two important initial steps in writing a review article are boundary identification (identifying a body of potentially relevant past research) and corpus construction (selecting research manuscripts to include in the review). We present a theory-as-discourse approach, which (1) creates a theory ecosystem of potentially relevant prior research using a citation-network approach to boundary identification; and (2) identifies manuscripts for consideration using machine learning or random selection. We demonstrate an instantiation of the theory as discourse approach through a proof-of-concept, which we call the automated detection of implicit theory (ADIT) technique. ADIT improves performance over the conventional approach as practiced in past technology acceptance model reviews (i.e., keyword search, sometimes manual citation chaining); it identifies a set of research manuscripts that is more comprehensive and at least as precise. Our analysis shows that the conventional approach failed to identify a majority of past research. Like the three blind men examining the elephant, the conventional approach distorts the totality of the phenomenon. ADIT also enables researchers to statistically estimate the number of relevant manuscripts that were excluded from the resulting review article, thus enabling an assessment of the review article’s representativeness

The academic advantage : gender disparities in patenting

We analyzed gender disparities in patenting by country, technological area, and type of assignee using the 4.6 million utility patents issued between 1976 and 2013 by the United States Patent and Trade Office (USPTO). Our analyses of fractionalized inventorships demonstrate that women’s rate of patenting has increased from 2.7% of total patenting activity to 10.8% over the nearly 40-year period. Our results show that, in every technological area, female patenting is proportionally more likely to occur in academic institutions than in corporate or government environments. However, women’s patents have a lower technological impact than that of men, and that gap is wider in the case of academic patents. We also provide evidence that patents to which women—and in particular academic women—contributed are associated with a higher number of International Patent Classification (IPC) codes and co-inventors than men. The policy implications of these disparities and academic setting advantages are discussed

Memory in network flows and its effects on spreading dynamics and community detection

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking, and spreading analysis although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and while we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking, and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.Comment: 23 pages and 16 figure

Men Set Their Own Cites High: Gender and Self-citation across Fields and over Time

How common is self-citation in scholarly publication, and does the practice vary by gender? Using novel methods and a data set of 1.5 million research papers in the scholarly database JSTOR published between 1779 and 2011, the authors find that nearly 10 percent of references are self-citations by a paper's authors. The findings also show that between 1779 and 2011, men cited their own papers 56 percent more than did women. In the last two decades of data, men self-cited 70 percent more than women. Women are also more than 10 percentage points more likely than men to not cite their own previous work at all. While these patterns could result from differences in the number of papers that men and women authors have published rather than gender-specific patterns of self-citation behavior, this gender gap in self-citation rates has remained stable over the last 50 years, despite increased representation of women in academia. The authors break down self-citation patterns by academic field and number of authors and comment on potential mechanisms behind these observations. These findings have important implications for scholarly visibility and cumulative advantage in academic careers.Comment: final published articl

Gender-based homophily in collaborations across a heterogeneous scholarly landscape

Using the corpus of JSTOR articles, we investigate the role of gender in collaboration patterns across the scholarly landscape by analyzing gender-based homophily--the tendency for researchers to co-author with individuals of the same gender. For a nuanced analysis of gender homophily, we develop methodology necessitated by the fact that the data comprises heterogeneous sub-disciplines and that not all authorships are exchangeable. In particular, we distinguish three components of gender homophily in collaborations: a structural component that is due to demographics and non-gendered authorship norms of a scholarly community, a compositional component which is driven by varying gender representation across sub-disciplines, and a behavioral component which we define as the remainder of observed homophily after its structural and compositional components have been taken into account. Using minimal modeling assumptions, we measure and test for behavioral homophily. We find that significant behavioral homophily can be detected across the JSTOR corpus and show that this finding is robust to missing gender indicators in our data. In a secondary analysis, we show that the proportion of female representation in a field is positively associated with significant behavioral homophily