  Well begun is half done. In the crowdfunding market, the early fundraising
performance of the project is a concerned issue for both creators and
platforms. However, estimating the early fundraising performance before the
project published is very challenging and still under-explored. To that end, in
this paper, we present a focused study on this important problem in a market
modeling view. Specifically, we propose a Graph-based Market Environment model
(GME) for estimating the early fundraising performance of the target project by
exploiting the market environment. In addition, we discriminatively model the
market competition and market evolution by designing two graph-based neural
network architectures and incorporating them into the joint optimization stage.
Finally, we conduct extensive experiments on the real-world crowdfunding data
collected from Indiegogo.com. The experimental results clearly demonstrate the
effectiveness of our proposed model for modeling and estimating the early
fundraising performance of the target project.
