|Title||Modeling Scientific Influence for Research Trending Topic Prediction|
|Affiliation||Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China.|
Peking Univ, MOE Key Lab Computat Linguist, Beijing, Peoples R China.
Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China.
|Publisher||THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE|
|Citation||THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE. 2018, 2111-2118.|
|Abstract||With the growing volume of publications in the Computer Science (CS) discipline, tracking the research evolution and predicting the future research trending topics are of great importance for researchers to keep up with the rapid progress of research. Within a research area, there are many top conferences that publish the latest research results. These conferences mutually influence each other and jointly promote the development of the research area. To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously. The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. We also demonstrate the interpretability and predictability of the proposed model by providing its answers to two questions of concern, i.e., what the next rising trending topics are and for each conference who the most influential peer is.|
|Appears in Collections:||计算语言学教育部重点实验室|