A General Framework For Task-Oriented Network Inference
Social and Information Networks
2017-05-03 v1
Abstract
We present a brief introduction to a flexible, general network inference framework which models data as a network space, sampled to optimize network structure to a particular task. We introduce a formal problem statement related to influence maximization in networks, where the network structure is not given as input, but learned jointly with an influence maximization solution.
Cite
@article{arxiv.1705.00645,
title = {A General Framework For Task-Oriented Network Inference},
author = {Ivan Brugere and Chris Kanich and Tanya Y. Berger-Wolf},
journal= {arXiv preprint arXiv:1705.00645},
year = {2017}
}
Comments
SIAM SDM Workshop on Inferring Networks from Non-Network Data, 2017