@article{fu2014academic,
  abstract = {By means of their academic publications, authors form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors select co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Academic Search and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to discover, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities—authors, publication venues, and institutions. We go beyond traditional metrics such as paper counts, citations, and h-index. Specifically, we define metrics such as },
  author = {Fu, TomZ.J. and Song, Qianqian and Chiu, DahMing},
  doi = {10.1007/s11192-014-1356-x},
  interhash = {a39d784173e693ac65979737e96c2a3c},
  intrahash = {de2f3434421912af52e355578e147b0a},
  issn = {0138-9130},
  journal = {Scientometrics},
  language = {English},
  number = 1,
  pages = {203-239},
  publisher = {Springer Netherlands},
  title = {The academic social network},
  url = {http://dx.doi.org/10.1007/s11192-014-1356-x},
  volume = 101,
  year = 2014
}