Abstract
This paper investigates the problem of finding author interest in co-author network through topic modeling with providing several performance evaluation measures. Intuitively, there are two types of explicit grouping exists in research papers (1) authors who have co-authored with author A in one document (subgroup) and (2) authors who have co-authored with author A in all documents (group). Traditional methods use graph-link structure by using keywords based matching and ignored semantics-based information, while topic modeling considered semantics-based information but ignored both types of explicit grouping e.g. State-of-the-art Author-Topic model used only one kind of explicit grouping single document (subgroup) for finding author interest. In this paper, we introduce Group-Author-Topic (GAT) modeling which exploits both types of grouping simultaneously. We compare four different topic modeling methods for same task on large DBLP dataset. We provide three performance measures for method evaluation from different domains which are; perplexity, entropy, and prediction ranking accuracy. We show the trade of between these performance evaluation measures. Experimental results demonstrate that our proposed method significantly outperformed the baselines in finding author interest. The trade of between used evaluation measures shows that they are equally useful for evaluating topic modeling methods.