As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.

How difficult is it for an early career academic to climb the ranks of their discipline? We tackle this question with a comprehensive bibliometric analysis of 57 disciplines, examining the publications of more than 5 million authors whose careers started between 1986 and 2008. We calibrate a simple random walk model over historical data of ranking mobility, which we use to 1) identify which strata of academic impact rankings are the most/least mobile and 2) study the temporal evolution of mobility. By focusing our analysis on cohorts of authors starting their careers in the same year, we find that ranking mobility is remarkably low for the top- and bottom-ranked authors and that this excess of stability persists throughout the entire period of our analysis. We further observe that mobility of impact rankings has increased over time, and that such rise has been accompanied by a decline of impact inequality, which is consistent with the negative correlation that we observe between such two quantities. These findings provide clarity on the opportunities of new scholars entering the academic community, with implications for academic policymaking.

The Great Gatsby Curve measures the relationship between income inequality and intergenerational income persistence. By using genealogical data of over 245 000 mentor–mentee pairs and their academic publications from 22 different disciplines, this study demonstrates that an academic Great Gatsby Curve exists as well, in the form of a positive correlation between academic impact inequality and the persistence of impact across academic generations. We also provide a detailed breakdown of academic persistence, showing that the correlation between the impact of mentors and that of their mentees has increased over time, indicating an overall decrease in academic intergenerational mobility. We analyse such persistence across a variety of dimensions, including mentorship types, gender and institutional prestige.

In 2009, many governments implemented austerity measures. This often resulted in restrictions on science funding, increasing competition for funds. Here, we study the allocation of funding across universities by using a competitiveness measure from complexity science based on the bipartite network of universities and scientific subjects. Drawing on a comprehensive data set of 43,430 UK-funded grants between 2006 and 2020, a period covering the onset and relaxation of austerity, we provide two main results. First, we find that the effect of austerity on university grant income depended on research competitiveness: highly competitive universities increased grant income under austerity, while low-ranked universities lost income. Then, we study the resilience of science to austerity exploiting the natural experiment arising from the 2015 UK general election, when austerity was relaxed unexpectedly. We discover that the resilience of researchers at less competitive universities was surprisingly high. The number and size of grant applications grew more after 2015 at less competitive universities, increasing their grant income. Overall, results show that our network-based measure is a better proxy for research competitiveness than aggregate grant income across scientific subjects.

Interdisciplinary research is fundamental when it comes to tackling complex problems in our highly interlinked world, and is on the rise globally. Yet, it is unclear why--in an increasingly competitive academic environment--one should pursue an interdisciplinary career given its recent negative press. Several studies have indeed shown that interdisciplinary research often achieves lower impact compared to more specialized work, and is less likely to attract funding. We seek to reconcile such evidence by analyzing a dataset of 44,419 research grants awarded between 2006 and 2018 from the seven national research councils in the UK. We compared the research performance of researchers with an interdisciplinary funding track record with those who have a specialized profile. We found that the former dominates the network of academic collaborations, both in terms of centrality and knowledge brokerage; but such a competitive advantage does not immediately translate into impact. Indeed, by means of a matched pair experimental design, we found that researchers who transcend between disciplines on average achieve lower impacts in their publications than the subject specialists in the short run, but eventually outperform them in funding performance, both in terms of volume and value. Our results suggest that launching an interdisciplinary career may require more time and persistence to overcome extra challenges, but can pave the way for a more successful endeavour.