Authorship Attribution with Support Vector Machines
Applied Intelligence
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Conference Mining via Generalized Topic Modeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning author-topic models from text corpora
ACM Transactions on Information Systems (TOIS)
Temporal expert finding through generalized time topic modeling
Knowledge-Based Systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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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.