Research Article
Topic-Aware Influence Maximization in Large Recommendation Social Networks
@INPROCEEDINGS{10.1007/978-3-319-73317-3_24, author={Jinghua Zhu and Qian Ming and Nan Wang}, title={Topic-Aware Influence Maximization in Large Recommendation Social Networks}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Influence maximization Topic-aware Recommendation social network}, doi={10.1007/978-3-319-73317-3_24} }- Jinghua Zhu
Qian Ming
Nan Wang
Year: 2018
Topic-Aware Influence Maximization in Large Recommendation Social Networks
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_24
Abstract
Influence maximization () is a problem of finding several influential individuals in a social network so that their influence spread is maximized under certain propagation model. In recommendation social network such as Douban, information diffuses with multiple origins: internal and external influence. Furthermore, pairs of individuals usually have different influence strength on different topics, information, ideas and rumors etc. In this paper, we focus on the topic-aware problem for large recommendation social networks. We propose a novel TSID propagation model to formulate the multiple topics diffusion in recommendation social networks. We propose TIP algorithm to solve the influence maximization problem under TSID propagation model. Our experiment results show that TSID model can well depict the mix information propagation process in recommendation social network, the TIP algorithm has competitive response time and influence spread.
