Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
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Abstract (arXiv)
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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 27 May 2021.
Builds on
24 of 52 references.
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Citing papers per year: 2022: 2.
2 papers.