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Understanding the dynamic mechanisms that drive the high-impact scientificwork (e.g., research papers, patents) is a long-debated research topic and hasmany important implications, ranging from personal career development andrecruitment search, to the jurisdiction of research resources. Recent advancesin characterizing and modeling scientific success have made it possible toforecast the long-term impact of scientific work, where data mining techniques,supervised learning in particular, play an essential role. Despite muchprogress, several key algorithmic challenges in relation to predictinglong-term scientific impact have largely remained open. In this paper, wepropose a joint predictive model to forecast the long-term scientific impact atthe early stage, which simultaneously addresses a number of these openchallenges, including the scholarly feature design, the non-linearity, thedomain-heterogeneity and dynamics. In particular, we formulate it as aregularized optimization problem and propose effective and scalable algorithmsto solve it. We perform extensive empirical evaluations on large, realscholarly data sets to validate the effectiveness and the efficiency of ourmethod.

The Child is Father of the Man: Foresee the Success at the Early Stage