Code for Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks (WWW 2020).
In this paper, we are motivated to investigate if resource-limited individuals discover strategic behaviors associated with high payoffs when producing collaborative/interactive content in social networks. We propose a novel framework of Dynamic Dual Attention Networks (DDAN) which models individuals' content production strategies through a generative process, under the influence of social interactions involved in the process. Extensive experimental results illustrate the model's effectiveness in user behavior modeling. We make three strong empirical findings: (1) Different strategies give rise to different social payoffs; (2) The best performing individuals exhibit stability in their preference over the discovered strategies, which indicates the emergence of strategic behavior; and (3) The stability of a user's preference is correlated with high payoffs.
The graph neural network model used in our paper. Refer to the corresponding folder for more details.
An empirical application of our framework to the DBLP academic dataset for qualitative analysis. Refer to the corresponding folder for more details.
@article{xiao2020discovering,
title={Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks},
author={Xiao, Yuxin and Krishnan, Adit and Sundaram, Hari},
journal={arXiv preprint arXiv:2003.03670},
year={2020}
}