@inproceedings{500964e3dd0542bdbee6f3e3d1fe9098,
title = "A co-ranking framework to select optimal seed set for influence maximization in heterogeneous network",
abstract = "The rising popularity of social media presents new opportunities for one of the enterprise{\textquoteright}s most important needs—selecting most influential individuals in viral marketing, which has attracted increasing attention in both academia and industry. Most recent algorithms of influence maximization have demonstrated remarkable successes, however their applications are limited to homogeneous networks. In this paper, we formulate the problem of influence maximization in heterogeneous network, and propose a co-ranking framework to simultaneously select seed sets with different types. This framework is flexible and could adequately takes advantage of additional information implicit in the heterogeneous structure. We conduct extensive experiments using the data collected from ACM Digital Library, and the experimental results show that both the quality and the running time of the proposed algorithm rival the existing algorithms.",
keywords = "Co-Ranking, Heterogeneous network, Influence maximization",
author = "Yashen Wang and Heyan Huang and Chong Feng and Xianxiang Yang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 17th Asia-PacificWeb Conference, APWeb 2015 ; Conference date: 18-09-2015 Through 20-09-2015",
year = "2015",
doi = "10.1007/978-3-319-25255-1_12",
language = "English",
isbn = "9783319252544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "141--153",
editor = "Reynold Cheng and Bin Cui and Zhenjie Zhang and Ruichu Cai and Jia Xu",
booktitle = "Web Technologies and Applications - 17th Asia-PacificWeb Conference,APWeb 2015, Proceedings",
address = "Germany",
}