@inproceedings{20674269c2bc4b0ebff33af8eac6d7c1,
title = "On the maximization of influence over an unknown social network",
abstract = "Influence maximization is a well-investigated problem which asks for key individuals who have significant influence in a given social network. This paper addresses this problem when the social network structure is hidden. We adopt the framework of influence learning from samples and build a neural network model to represent the information diffusion process. Based on the model, we propose two new algorithms NeuGreedy and NeuMax. NeuGreedy simulates the traditional greedy algorithm whilst NeuMax utilizes the weights of connections between neurons. We test the algorithms on both synthetic and real-world datasets. The results verify the effectiveness of the proposed methods as compared to existing algorithms with or without the network structure.",
keywords = "Hidden network structure, Influence maximization, Machine learning, Neural network, Social influence, Social network",
author = "Bo Yan and Fanku Meng and Kexiu Song and Yiping Liu and Jiamou Liu and Hongyi Su",
note = "Publisher Copyright: {\textcopyright} 2019 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.; 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 ; Conference date: 13-05-2019 Through 17-05-2019",
year = "2019",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "2279--2281",
booktitle = "18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019",
}