  In the online innovation market, the fund-raising performance of the start-up
project is a concerning issue for creators, investors and platforms.
Unfortunately, existing studies always focus on modeling the fund-raising
process after the publishment of a project but the predicting of a project
attraction in the market before setting up is largely unexploited. Usually,
this prediction is always with great challenges to making a comprehensive
understanding of both the start-up project and market environment. To that end,
in this paper, we present a focused study on this important problem from a
market graph perspective. Specifically, we propose a Graph-based Market
Environment (GME) model for predicting the fund-raising performance of the
unpublished project by exploiting the market environment. In addition, we
discriminatively model the project competitiveness and market preferences by
designing two graph-based neural network architectures and incorporating them
into a joint optimization stage. Furthermore, to explore the information
propagation problem with dynamic environment in a large-scale market graph, we
extend the GME model with parallelizing competitiveness quantification and
hierarchical propagation algorithm. Finally, we conduct extensive experiments
on real-world data. The experimental results clearly demonstrate the
effectiveness of our proposed model.
