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

• URL: http://arxiv.org/abs/2105.12918v1
• Date: Thu, 27 May 2021 02:39:30 GMT
• Title: Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
• Authors: Likang Wu, Zhi Li, Hongke Zhao, Qi Liu, Enhong Chen
• Abstract summary: We propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment. 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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
• Score: 58.353799280109904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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.

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