ABSTRACT
It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.
- Barabási, A.-L. and Albert, R. 1999. Emergence of Scaling in Random Networks. Science, 286: 509--512.Google ScholarCross Ref
- Brin, S., and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst., 30, 1--7, (Apr. 1998) 107--117. Google ScholarDigital Library
- Chen, P., Xie, H., Maslov, S., and Redner, S. 2007. Finding scientific gems with Google's PageRank algorithm. J. Informetrics, 1, 1 (Jun. 2007), 8--15.Google ScholarCross Ref
- Das, S., Mitra, P., and Lee Giles, C. 2011. Ranking Authors in Digital Libraries. In Proc. JCDL'11, 251--254. Google ScholarDigital Library
- Ding, Y., Yan, E., Frazho, R., and Caverlee, J. 2009. PageRank for Ranking Authors in Co-citation Networks. J. Am. Soc. Inf. Sci. Technol., 60, 11 (Jun. 2009), 2229--2243. Google ScholarDigital Library
- Garfield, E. 1972. Citation analysis as a tool in journal eval-uation. Science, 178, 60 (Nov. 1972), 471--479.Google ScholarCross Ref
- Hirsch, J. E. 2005. An index to quantify an individual's sci-entific research output. Proc. Natl. Acad. Sci., 102, 46 (Nov. 2005), 16569--4.Google ScholarCross Ref
- Jensen, C. S., Cao, X., and Cong, G. 2010. Mining Significant Semantic Locations from GPS Data. In Proc. VLDB, 3, 1--2 (Sep. 2010), 1009--1020. Google ScholarDigital Library
- Katerattanakul, P., Han, B., and Hong, S. 2003. Objective quality ranking of computing journals. Commun. ACM, 46, 10 (Oct. 2003), 111--114. Google ScholarDigital Library
- Kleinberg, J. M. 1999. Authoritative Sources in a Hyperlinked Environment. J. ACM. 46, 5 (Sep. 1999), 604--632. Google ScholarDigital Library
- Lefebvre, M. 2006. Applied Stochastic Processes. Springer.Google Scholar
- Lempel, R., and Moran, S. 2001. SALSA: The Stochastic Approach for Link-Structure Analysis. ACM Trans. Internet Tech. 19, 2 (Apr. 2001), 131--169. Google ScholarDigital Library
- Li, X., Liu, B., and Yu, P. 2008. Time Sensitive Ranking with Application to Publication Search. In Proc. ICDM'08, 893--898. Google ScholarDigital Library
- Manning, C. D., Raghavan, R., and Schütze, H. 2008. Introduction to Information retrieval. Cambridge University Press. Google ScholarDigital Library
- Nerur, S., Sikora, R., Mangalaraj, G., and Balijepally, V. 2005. Assessing the relative influence of journals in a citation network. Commun. ACM, 48, 11 (Nov. 2005), 71--74. Google ScholarDigital Library
- Newman, M. E. J. 2002. Assortative Mixing in Networks. Phys. Rev. Lett., 89, 20: 208701--5.Google ScholarCross Ref
- Ng, A. Y., Zheng, A. X., and Jordan, M. I. 2001. Stable Al-gorithms for Link Analysis. In Proc. SIGIR'01, 258--266. Google ScholarDigital Library
- Ng, M. K., Li, X., and Ye, Y. 2011. MultiRank: co-ranking for objects and relations in multi-relational data. In Proc. KDD'11, 1217--1225. Google ScholarDigital Library
- Radicchi, F., Fortunato, S., Markines, B., and Vespignani, A. 2009. Diffusion of scientific credits and the ranking of scientists. Phys. Rev. E, 80, 5 (Nov. 2009), 056103--12.Google ScholarCross Ref
- Radev, D. R., Muthukrishnan, P., and Qazvinian, V. 2009. The ACL Anthology Network. In Proc. NLPIR4DL'09, 54--61. Google ScholarDigital Library
- Sayyadi, H., and Getoor, L. 2009. FutureRank: Ranking Scientific Articles by Predicting their Future PageRank. In Proc. SDM'09. 533--544.Google Scholar
- Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z. 2008. ArnetMiner: extracting academic social networks. In Proc. KDD'08. 990--998. Google ScholarDigital Library
- Walker, D., Xie, H., Yan, K.-K., and Maslov, S. 2007. Ranking scientific publications using a model of network traffic. J. Stat. Mech., 7 (Jun. 2007), 06010--9.Google ScholarCross Ref
- Yan, E., and Ding, Y. 2009. Applying centrality measures to impact analysis: A coauthorship network analysis. J. Am. Soc. Inf. Sci. Technol. 60, 10 (Oct. 2009), 2107--2118. Google ScholarDigital Library
- Yan, S., and Lee, D.-W. 2007. Toward Alternative Measures for Ranking Venues: A Case of Database Research Community. In Proc. JCDL'07, 235--244. Google ScholarDigital Library
- Zhou, D., Orshanskiy, S. A., Zha, H., and Lee Giles, C. 2007. Co-Ranking Authors and Documents in a Heterogeneous Network. In Proc. ICDM'07, 739--744. Google ScholarDigital Library
- Zhuge, H., and Zhang, J. 2010. Topological Centrality and Its e-Science Applications. J. Am. Soc. Inf. Sci. Technol., 61, 9 (May 2010), 1824--1841. Google ScholarDigital Library
Index Terms
- Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
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