Ranking authors in digital libraries

Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document's association with venues and citation links with other documents. We propose graph-based models that accommodate multiple sources of evidence in a PageRank-like algorithm for ranking experts. Our studies on two publicly-available datasets indicate that our model despite being general enough to be directly useful for ranking other types of objects performs on par with probabilistic models commonly used for expert ranking.

[1]  M. de Rijke,et al.  Formal models for expert finding in enterprise corpora , 2006, SIGIR.

[2]  Vagelis Hristidis,et al.  ObjectRank: a system for authority-based search on databases , 2006, SIGMOD Conference.

[3]  Wei-Ying Ma,et al.  Object-level ranking: bringing order to Web objects , 2005, WWW '05.

[4]  Hongbo Deng,et al.  Formal Models for Expert Finding on DBLP Bibliography Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[5]  Sergei Maslov,et al.  Ranking scientific publications using a model of network traffic , 2006, ArXiv.

[6]  Taher H. Haveliwala.  Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[7]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. de Rijke,et al.  Broad expertise retrieval in sparse data environments , 2007, SIGIR.

[9]  Yi Zhang,et al.  Graph-based ranking algorithms for e-mail expertise analysis , 2003, DMKD '03.

[10]  Sheldon M. Ross,et al.  Introduction to Probability Models, Eighth Edition , 1972 .

[11]  Gene H. Golub,et al.  Computing PageRank using Power Extrapolation , 2003 .

[12]  Johan Bollen,et al.  Co-authorship networks in the digital library research community , 2005, Inf. Process. Manag..

[13]  William W. Cohen,et al.  Adaptive graph walk based similarity measures in entity-relation graphs , 2008 .

[14]  M. de Rijke,et al.  Determining Expert Profiles (With an Application to Expert Finding) , 2007, IJCAI.

[15]  Djoerd Hiemstra,et al.  Modeling multi-step relevance propagation for expert finding , 2008, CIKM '08.

[16]  Soumen Chakrabarti,et al.  Learning to rank networked entities , 2006, KDD '06.

[17]  S. Sudarshan,et al.  Keyword searching and browsing in databases using BANKS , 2002, Proceedings 18th International Conference on Data Engineering.

[18]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[19]  Ruoming Jin,et al.  A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[20]  Dan Roth,et al.  Citation Author Topic Model in Expert Search , 2010, COLING.

[21]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[22]  Hongyuan Zha,et al.  Co-ranking Authors and Documents in a Heterogeneous Network , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[23]  Craig MacDonald,et al.  Voting for candidates: adapting data fusion techniques for an expert search task , 2006, CIKM '06.

[24]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[25]  Wolfgang Nejdl,et al.  A Vector Space Model for Ranking Entities and Its Application to Expert Search , 2009, ECIR.

[26]  Andrew McCallum,et al.  Mining a digital library for influential authors , 2007, JCDL '07.