@inproceedings{c7f003225d2148178868b7c12d8035ca,
title = "Diversified ranking on large graphs: An optimization viewpoint",
abstract = "Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.",
keywords = "Diversity, Graph mining, Ranking, Scalability",
author = "Hanghang Tong and Jingrui He and Zhen Wen and Ravi Konuru and Lin, {Ching Yung}",
year = "2011",
doi = "10.1145/2020408.2020573",
language = "English (US)",
isbn = "9781450308137",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "1028--1036",
booktitle = "Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11",
note = "17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011 ; Conference date: 21-08-2011 Through 24-08-2011",
}