Localized Ranking in Social and Information Networks

Joyce Jiyoung WHANG  Yunseob SHIN  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.2   pp.547-551
Publication Date: 2018/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8178
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
ranking,  PageRank,  HITS,  clustering,  network analysis,  

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In social and information network analysis, ranking has been considered to be one of the most fundamental and important tasks where the goal is to rank the nodes of a given graph according to their importance. For example, the PageRank and the HITS algorithms are well-known ranking methods. While these traditional ranking methods focus only on the structure of the entire network, we propose to incorporate a local view into node ranking by exploiting the clustering structure of real-world networks. We develop localized ranking mechanisms by partitioning the graphs into a set of tightly-knit groups and extracting each of the groups where the localized ranking is computed. Experimental results show that our localized ranking methods rank the nodes quite differently from the traditional global ranking methods, which indicates that our methods provide new insights and meaningful viewpoints for network analysis.