Implicit Multi-feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

TL;DR

This paper introduces an implicit multi-feature learning model that predicts the future impact of research institutions by integrating factors like author influence, geographic location, and state GDP, revealing underlying drivers of impact evolution.

Contribution

The paper proposes a novel multi-feature learning approach that combines multiple factors to predict institutional impact changes over time, advancing beyond previous reliance on historical relevance scores.

Findings

The impact of institutions is influenced by authors' influence, location, and economic factors.

The model accurately predicts future impact based on integrated features.

Identified key drivers of impact evolution in research institutions.

Abstract

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.