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Estimating fund-raising performance for start-up projects from a market graph perspective
Pattern Recognition ( IF 8 ) Pub Date : 2021-07-24 , DOI: 10.1016/j.patcog.2021.108204
Likang Wu 1 , Zhi Li 1 , Hongke Zhao 2 , Qi Liu 1 , Enhong Chen 1
Affiliation  

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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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



中文翻译:

从市场图的角度估算初创项目的筹资业绩

在网络创新市场,初创项目的融资表现是创作者、投资者和平台都关心的问题。不幸的是,现有的研究总是侧重于对项目发布后的筹资过程进行建模,但在项目启动前对市场吸引力的预测基本上没有得到充分利用。通常,这种预测对于全面了解初创项目和市场环境总是面临着巨大的挑战。为此,在本文中,我们从市场图表的角度对这一重要问题进行了重点研究。具体而言,我们提出了一种基于图的市场环境(GME)模型,通过利用市场环境来预测未公开项目的融资表现。此外,我们对通过设计两个基于图的神经网络架构并将它们整合到一个联合优化阶段来预测竞争力市场偏好。此外,为了探索大规模市场图中动态环境的信息传播问题,我们扩展了 GME 模型,并行化竞争力量化和分层传播算法。最后,我们对真实世界的数据进行了广泛的实验。实验结果清楚地证明了我们提出的模型的有效性。

更新日期:2021-08-03
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