TY - GEN
T1 - IPath
T2 - 16th SIAM International Conference on Data Mining 2016, SDM 2016
AU - Li, Liangyue
AU - Tong, Hanghang
AU - Tang, Jie
AU - Fan, Wei
N1 - Funding Information:
This work is partially supported by the National Science Foundation under Grant No. IIS1017415, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, by National Institutes of Health under the grant number R01LM011986, Region II University Transportation Center under the project number 49997-33 25 and a Baidu gift. Jie Tang is supported by the National High-tech R&D Program (No. 2014AA015103), National Basic Research Program of China (No. 2014CB340506), and a research fund supported by Huawei Inc.
Publisher Copyright:
Copyright © by SIAM.
PY - 2016
Y1 - 2016
N2 - Forecasting the success of scientific work has been attracting extensive research attention in the recent years. It is often of key importance to foresee the pathway to impact for scholarly entities for (1) tracking research frontier, (2) invoking an early intervention and (3) proactively allocating research resources. Many recent progresses have been seen in modeling the long-term scientific impact for point prediction. However, challenges still remain when it comes to forecasting the impact pathway. In this paper, we propose a novel predictive model to collectively achieve a set of design objectives to address these challenges, including prediction consistency and parameter smoothness. Extensive empirical evaluations on real scholarly data validate the effectiveness of the proposed model.
AB - Forecasting the success of scientific work has been attracting extensive research attention in the recent years. It is often of key importance to foresee the pathway to impact for scholarly entities for (1) tracking research frontier, (2) invoking an early intervention and (3) proactively allocating research resources. Many recent progresses have been seen in modeling the long-term scientific impact for point prediction. However, challenges still remain when it comes to forecasting the impact pathway. In this paper, we propose a novel predictive model to collectively achieve a set of design objectives to address these challenges, including prediction consistency and parameter smoothness. Extensive empirical evaluations on real scholarly data validate the effectiveness of the proposed model.
UR - http://www.scopus.com/inward/record.url?scp=84991678407&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991678407&partnerID=8YFLogxK
U2 - 10.1137/1.9781611974348.53
DO - 10.1137/1.9781611974348.53
M3 - Conference contribution
AN - SCOPUS:84991678407
T3 - 16th SIAM International Conference on Data Mining 2016, SDM 2016
SP - 468
EP - 476
BT - 16th SIAM International Conference on Data Mining 2016, SDM 2016
A2 - Venkatasubramanian, Sanjay Chawla
A2 - Meira, Wagner
PB - Society for Industrial and Applied Mathematics Publications
Y2 - 5 May 2016 through 7 May 2016
ER -