Estimating fund-raising performance for start-up projects from a market graph perspective

In the online innovation market, the fund-raising performance of the start-up project is a concerning is-sue 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 perspec-tive. 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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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 ex-plore 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 al-gorithm. Finally, we conduct extensive experiments on real-world data. The experimental results clearly demonstrate the effectiveness of our proposed model. (c) 2021 Published by Elsevier Ltd.

Automated summary

This paper presents a focused study on predicting the fund-raising performance of unpublished projects in the online innovation market from a market graph perspective. The proposed Graph-based Market Environment (GME) model discriminatively models project competitiveness and market preferences using graph-based neural network architectures. Extensive experiments show the effectiveness of the proposed model.