AMiner: Search and Mining of Academic Social Networks

AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis, and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.

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

[2]  Raymond J. Mooney,et al.  A probabilistic framework for semi-supervised clustering , 2004, KDD.

[3]  Thomas L. Griffiths,et al.  Probabilistic author-topic models for information discovery , 2004, KDD.

[4]  Jimeng Sun,et al.  Cross-domain collaboration recommendation , 2012, KDD.

[5]  Yizhou Sun,et al.  Co-Evolution of Multi-Typed Objects in Dynamic Star Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Hong Yang,et al.  Profiling Web users using big data , 2018, Social Network Analysis and Mining.

[7]  Jie Tang,et al.  A Combination Approach to Web User Profiling , 2010, TKDD.

[8]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[9]  Feng Xia,et al.  Mining advisor-advisee relationships in scholarly big data: A deep learning approach , 2016, 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL).

[10]  Jon M. Kleinberg,et al.  Transfer Learning to Infer Social Ties across Heterogeneous Networks , 2016, ACM Trans. Inf. Syst..

[11]  Jiawei Han,et al.  Mining advisor-advisee relationships from research publication networks , 2010, KDD.

[12]  Hanghang Tong,et al.  Panther: Fast Top-k Similarity Search on Large Networks , 2015, KDD.

[13]  Yang Song,et al.  An Overview of Microsoft Academic Service (MAS) and Applications , 2015, WWW.

[14]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[15]  Ruoming Jin,et al.  Topic level expertise search over heterogeneous networks , 2010, Machine Learning.

[16]  Jie Tang,et al.  Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. , 2018, KDD.

[17]  Jie Tang,et al.  Social Network Extraction of Academic Researchers , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[18]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[19]  Yuxiao Dong,et al.  DeepInf : Modeling Influence Locality in Large Social Networks , 2018 .

[20]  J. Carroll,et al.  Jena: implementing the semantic web recommendations , 2004, WWW Alt. '04.