Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

TL;DR

This paper introduces a graph-based model to predict the fundraising success of unpublished start-up projects by analyzing market environment and project competitiveness, addressing a gap in pre-launch prediction methods.

Contribution

The paper proposes a novel Graph-based Market Environment (GME) model with neural network architectures for predicting start-up project performance before launch.

Findings

The GME model effectively predicts fundraising performance on real-world data.

Discriminative modeling of project competitiveness improves prediction accuracy.

Hierarchical propagation enhances understanding of information flow in large-scale market graphs.

Abstract

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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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.