Jie Tang
ABSTRACT
In this talk, I will present a novel academic search and mining system, AMiner, the second generation of the ArnetMiner system. Different from traditional academic search systems that focus on document (paper) search, AMiner aims to provide a systematic modeling approach for researchers (authors), ultimately to gain a deep understanding of the big (heterogeneous) network formed by authors, papers they have published, and venues they published those papers. The system extracts researchers' profiles automatically from the Web and integrates the researcher profiles with publication papers after name disambiguation. For now, the system has collected a big scholar data with more than 130,000,000 researcher profiles and 100,000,000 papers from multiple publication databases. We also developed an approach named COSNET to connect AMiner with several professional social networks such as LinkedIn and VideoLectures, which significantly enriches the metadata of the scholarly data. Based on the integrated big scholar data, we devise a unified topic modeling approach for modeling the different entities (authors, papers, venues) simultaneously and provide a topic-level expertise search by leveraging the modeling results. In addition, AMiner offers a set of researcher-centered functions including social influence analysis, influence visualization, collaboration recommendation, relationship mining, similarity analysis and community evolution. The system has been put into operation since 2006 and has attracted more than 7,000,000 independent IP accesses from over 200 countries/regions.
- L. Shi, H. Tong, J. Tang, and C. Lin. Vegas: Visual influence graph summarization on citation networks. IEEE TKDE, 2015. Google Scholar
Digital Library
- Y. Sun, J. Tang, J. Han, C. Chen, and M. Gupta. Co-evolution of multi-typed objects in dynamic star networks. IEEE TKDE, 26(12):2942--2955, 2014.Google ScholarCross Ref
- J. Tang, A. Fong, B. Wang, and J. Zhang. A unified probabilistic framework for name disambiguation in digital library. IEEE TKDE, 24(6):975--987, 2012. Google ScholarDigital Library
- J. Tang, T. Lou, J. Kleinberg, and S. Wu. Transfer link prediction across heterogeneous social networks. ACM TOIS, 2015.Google Scholar
- J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In KDD'09, pages 807--816, 2009. Google ScholarDigital Library
- J. Tang, S. Wu, J. Sun, and H. Su. Cross-domain collaboration recommendation. In KDD'12, pages 1285--1294, 2012. Google ScholarDigital Library
- J. Tang, L. Yao, D. Zhang, and J. Zhang. A combination approach to web user profiling. ACM TKDD, 5(1):1--44, 2010. Google ScholarDigital Library
- J. Tang, J. Zhang, R. Jin, Z. Yang, K. Cai, L. Zhang, and Z. Su. Topic level expertise search over heterogeneous networks. Machine Learning Journal, 82(2):211--237, 2011. Google ScholarDigital Library
- J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD'08, pages 990--998, 2008. Google ScholarDigital Library
- C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. Mining advisor-advisee relationships from research publication networks. In KDD'10, pages 203--212, 2010. Google ScholarDigital Library
- J. Zhang, J. Tang, C. Ma, H. Tong, Y. Jing, and J. Li. Panther: Fast top-k similarity search on large networks. In KDD'15, pages 1445--1454, 2015. Google ScholarDigital Library
- Y. Zhang, J. Tang, Z. Yang, J. Pei, and P. Yu. Cosnet: Connecting heterogeneous social networks with local and global consistency. In KDD'15, pages 1485--1494, 2015. Google ScholarDigital Library
Index Terms
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AMiner: Toward Understanding Big Scholar Data
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