TY - GEN
T1 - Tri-Rank
T2 - 26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014
AU - Liu, Zhirun
AU - Huang, Heyan
AU - Wei, Xiaochi
AU - Mao, Xianling
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/12
Y1 - 2014/12/12
N2 - Recently, authority ranking has received increasing interests in both academia and industry, and it is applicable to many problems such as discovering influential nodes and building recommendation systems. Various graph-based ranking approaches like PageRank have been used to rank authors and papers separately in homogeneous networks. In this paper, we take venue information into consideration and propose a novel graph-based ranking framework, Tri-Rank, to co-rank authors, papers and venues simultaneously in heterogeneous networks. This approach is a flexible framework and it ranks authors, papers and venues iteratively in a mutually reinforcing way to achieve a more synthetic, fair ranking result. We conduct extensive experiments using the data collected from ACM Digital Library. The experimental results show that Tri-Rank is more effective and efficient than the state-of-the-art baselines including PageRank, HITS and Co-Rank in ranking authors. The papers and venues ranked by Tri-Rank also demonstrate that Tri-Rank is rational.
AB - Recently, authority ranking has received increasing interests in both academia and industry, and it is applicable to many problems such as discovering influential nodes and building recommendation systems. Various graph-based ranking approaches like PageRank have been used to rank authors and papers separately in homogeneous networks. In this paper, we take venue information into consideration and propose a novel graph-based ranking framework, Tri-Rank, to co-rank authors, papers and venues simultaneously in heterogeneous networks. This approach is a flexible framework and it ranks authors, papers and venues iteratively in a mutually reinforcing way to achieve a more synthetic, fair ranking result. We conduct extensive experiments using the data collected from ACM Digital Library. The experimental results show that Tri-Rank is more effective and efficient than the state-of-the-art baselines including PageRank, HITS and Co-Rank in ranking authors. The papers and venues ranked by Tri-Rank also demonstrate that Tri-Rank is rational.
KW - Authority ranking
KW - Mutual reinforce
KW - heterogeneous network
UR - http://www.scopus.com/inward/record.url?scp=84946548977&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2014.80
DO - 10.1109/ICTAI.2014.80
M3 - Conference contribution
AN - SCOPUS:84946548977
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 493
EP - 500
BT - Proceedings - 2014 IEEE 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014
PB - IEEE Computer Society
Y2 - 10 November 2014 through 12 November 2014
ER -