@inproceedings{a18b306f25f7490aa361d0b0d0f29932,
title = "Multimodal learning based approaches for link prediction in social networks",
abstract = "The link prediction problem in social networks is to estimate the value of the link that can represent relationship between social members. Researchers have proposed several methods for solving link prediction and a number of features have been used. Most of these models are learned with only considering the features from one kind of data. In this paper, by considering the data from link network structure and user comment, both of which could imply the concept of link value, we propose multimodal learning based approaches to predict the link values. The experiment results done on dataset from typical social networks show that our model could learn the joint representation of these datas properly, and the method MDBN outperforms other state-of-art link prediction methods.",
keywords = "Deep Belief Networks, Link prediction, Multimodal learning, Social networks, Support vector classifier",
author = "Feng Liu and Bingquan Liu and Chengjie Sun and Ming Liu and Xiaolong Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 4th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2015 ; Conference date: 09-10-2015 Through 13-10-2015",
year = "2015",
doi = "10.1007/978-3-319-25207-0\_11",
language = "英语",
isbn = "9783319252063",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "123--133",
editor = "Heng Ji and Dongyan Zhao and Yansong Feng and Juanzi Li",
booktitle = "Natural Language Processing and Chinese Computing - 4th CCF Conference, NLPCC 2015, Proceedings",
address = "德国",
}