Title :
Academic network analysis: A joint topic modeling approach
Author :
Zaihan Yang ; Liangjie Hong ; Davison, Brian D.
Author_Institution :
Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA
Abstract :
We propose a novel probabilistic topic model that jointly models authors, documents, cited authors, and venues simultaneously in one integrated framework, as compared to previous work which embeds fewer components. This model is designed for three typical applications in academic network analysis: the problems of expert ranking, cited author prediction and venue prediction. Experiments based on two real world data sets demonstrate the model to be effective, and it outperforms several state-of-the-art algorithms in all three applications.
Keywords :
Internet; probability; social networking (online); academic network analysis; cited author prediction; expert ranking; integrated framework; joint topic modeling approach; probabilistic topic model; social network; venue prediction; Analytical models; Hip; Social network services; Evaluation; Expert Ranking; Prediction; Topic Models;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location :
Niagara Falls, ON