Learning Distributed Representations of Users for Source Detection in Online Social Networks
Abstract
In this paper, we study the problem of source detection in the context of information diffusion through online social networks. We propose a representation learning approach that leads to a robust model able to deal with the sparsity of the data. From learned continuous projections of the users, our approach is able to efficiently predict the source of any newly observed diffusion episode. Our model does not rely neither on a known diffusion graph nor on a hypothetical probabilistic diffusion law, but directly infers the source from diffusion episodes. It is also less complex than alternative state of the art models. It showed good performances on artificial and real-world datasets, compared with various state of the art baselines.
Cite
Text
Bourigault et al. "Learning Distributed Representations of Users for Source Detection in Online Social Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46227-1_17Markdown
[Bourigault et al. "Learning Distributed Representations of Users for Source Detection in Online Social Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/bourigault2016ecmlpkdd-learning/) doi:10.1007/978-3-319-46227-1_17BibTeX
@inproceedings{bourigault2016ecmlpkdd-learning,
title = {{Learning Distributed Representations of Users for Source Detection in Online Social Networks}},
author = {Bourigault, Simon and Lamprier, Sylvain and Gallinari, Patrick},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {265-281},
doi = {10.1007/978-3-319-46227-1_17},
url = {https://mlanthology.org/ecmlpkdd/2016/bourigault2016ecmlpkdd-learning/}
}