页面排名
秩(图论)
排名(信息检索)
计算机科学
学习排名
图形
情报检索
链路分析
排序倒数
同种类的
理论计算机科学
数据挖掘
数据科学
数学
组合数学
作者
Zhirun Liu,Heyan Huang,Xiaochi Wei,Xian-Ling Mao
标识
DOI:10.1109/ictai.2014.80
摘要
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.
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