@inproceedings{4ef839df3fcf4a318b22f9f1c3645a04,
title = "Maximal influence spread for social network based on mapreduce",
abstract = "Due to its importance, influence spread maximization problem for social network has been solved by a number of algorithms. However, when it comes to the scalabilities, existing algorithms are not efficient enough to cope with real-world social networks, which are often big networks. To handle big social networks, we propose parallelized influence spread algorithms. Using Map-Reduce in Hadoop as the platform, we proposed Parallel DAGIS algorithm, a parallel influence spread maximization algorithm. Considering information loss in Parallel DAGIS algorithm, we also develop a Parallel Sampling algorithm and change DFS to BFS during search process. Considering two or even more hops neighbor nodes, we further improve accuracy of DHH. Experimental results show that efficiency has been improved, when coping with big social network, by using Parallel DAGIS algorithm and Parallel Sampling algorithm. The accuracy of DHH has been improved by taking into account more than two hops neighbors.",
keywords = "Influence spread, Map-reduce, Social Network",
author = "Qiqi Shi and Hongzhi Wang and Dong Li and Xinfei Shi and Chen Ye and Hong Gao",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015 ; Conference date: 10-01-2015 Through 12-01-2015",
year = "2015",
language = "英语",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer New York LLC",
pages = "128--136",
editor = "Hongzhi Wang and Wanxiang Che and Zhaowen Qiu and Zhongyuan Han and Junyu Lin and Haoliang Qi and Zeguang Lin and Leilei Kong",
booktitle = "Intelligent Computation in Big Data Era - International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Proceedings",
}