Paper Info
Title
The Child is Father of the Man: Foresee the Success at the Early Stage.
Abstract
Understanding the dynamic mechanisms that drive the high-impact scientific work (e.g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources. Recent advances in characterizing and modeling scientific success have made it possible to forecast the long-term impact of scientific work, where data mining techniques, supervised learning in particular, play an essential role. Despite much progress, several key algorithmic challenges in relation to predicting long-term scientific impact have largely remained open. In this paper, we propose a joint predictive model to forecast the long-term scientific impact at the early stage, which simultaneously addresses a number of these open challenges, including the scholarly feature design, the non-linearity, the domain-heterogeneity and dynamics. In particular, we formulate it as a regularized optimization problem and propose effective and scalable algorithms to solve it. We perform extensive empirical evaluations on large, real scholarly data sets to validate the effectiveness and the efficiency of our method.
Year
DOI
Venue
2015
10.1145/2783258.2783340
KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Sydney NSW Australia August, 2015
Keywords
Field
DocType
long term impact prediction,joint predictive model
Data mining,Computer science,Supervised learning,Jurisdiction,Artificial intelligence,Scalable algorithms,Career development,Optimization problem,Machine learning,Feature design
Journal
Volume
ISBN
Citations 
abs/1504.00948
978-1-4503-3664-2
11
PageRank 
References 
Authors
0.58
26
2
Name
Order
Citations
PageRank
Liangyue Li113710.68
Hanghang Tong23560202.37