Loan Recommendation in P2P Lending Investment Networks: A Hybrid Graph Convolution Approach

Chai, Yibo; Cong, Yongqi; Bai, Lu; Cui, Lixin

doi:10.1109/ieem44572.2019.8978499

Cited by 2 publications

(2 citation statements)

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“…GNN has been implemented into several important applications. For recommendation systems, many works utilized graph neural networks to capture the interest similarities among quite a number of customers or the content coherences of every item pair [30,31,32]. For social network modeling, the community event discovery and influence propagation were learned by designed GNNs [33,34,35].…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…For social network modeling, the community event discovery and influence propagation were learned by designed GNNs [33,34,35]. For P2P lending, the loan requirements and investing lenders can also be matched by hybrid GCN [32]. Compared with traditional network embedding methods, the end-to-end GNN-based models were more suitable for supporting the platform's running [34].…”

Section: Graph Neural Networkmentioning

confidence: 99%

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

Zhao

et al. 2021

Preprint

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we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.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