The Journal of Machine Learning Research
Bibliometric impact measures leveraging topic analysis
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Topic-driven multi-type citation network analysis
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
On identifying academic homepages for digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Ranking authors in digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Intra-firm information flow: a content-structure perspective
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Similar researcher search in academic environments
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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When browsing a digital library of research papers, it is natural to ask which authors are most influential in a particular topic. We present a probabilistic model that ranks authors based on their influence in particular areas of scientific research. This model combines several sources of information: citation information between documents as represented by PageRank scores, authorship data gathered through automatic information extraction, and the words in paper abstracts. We compare the performance of a topic model versus a smoothed language model by assessing the number of major award winners in the resulting ranked list of researchers.