Designing, Analyzing and Exploiting Stake-Based Social Networks

It is widely recognized that stakeholder information can provide important knowledge about stock investments, and an increasing number of countries require that such information is publicly available. In this paper, we present a novel way to exploit stakeholder information by using it to construct stake-based social networks, namely, StakeNet. We also provide a visualization tool that displays socio-centric and ego-centric views of the networks. In addition, we analyze stakeholders´ static and dynamic behavior patterns in StakeNet, and demonstrate that most of StakeNet´s properties are similar to those of a typical social network, except that the in-degree distribution does not follow a power law distribution. Finally, we demonstrate two applications of StakeNet by exploiting it to identify important companies and to group companies together. The experiments show that our results are highly consistent with the outcomes generated by human experts. Source code, dataset, and resources are available at http://www.csie.ntu.edu.tw/~d97944007/stakenet/.