Loading...
Paper Number
1958
Paper Type
Completed
Description
Identifying startups with the highest potential for success is a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Specifically, we construct a Heterogeneous Venture Information Network (HVIN) using raw business data and deem the prediction a node classification task. Our model integrates theory-guided semantic meta-paths, firm demographics, sampling-based self-attention, and centrality encoding to overcome certain constraints of existing GNNs. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics. Our study also includes a comprehensive interpretation analysis to provide investors with an essential understanding for better decision-making.
Recommended Citation
zhang, shengming; Zhong, Hao; Ge, Yong; and Xiong, Hui, "Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks" (2023). ICIS 2023 Proceedings. 10.
https://aisel.aisnet.org/icis2023/dab_sc/dab_sc/10
Bring Me a Good One: Seeking High-potential Startups using Heterogeneous Venture Information Networks
Identifying startups with the highest potential for success is a complex task, necessitating the examination of various information sources, including firm demographics, management team composition, and financial performance. The effectiveness of existing methodologies, such as feature-based and network-topological approaches, is limited for predicting high-potential startups. In response, we propose a novel Venture Graph Neural Network (VenGNN) model, leveraging Heterogeneous Information Networks (HIN) and Graph Neural Networks (GNN) techniques to address the prediction problem. Specifically, we construct a Heterogeneous Venture Information Network (HVIN) using raw business data and deem the prediction a node classification task. Our model integrates theory-guided semantic meta-paths, firm demographics, sampling-based self-attention, and centrality encoding to overcome certain constraints of existing GNNs. Our experimental analysis reveals that VenGNN outperforms state-of-the-art models by 15-20% across a wide range of performance metrics. Our study also includes a comprehensive interpretation analysis to provide investors with an essential understanding for better decision-making.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
13-DataAnalytics