MGT Model

This is the source code for paper Graph Neural Network Based VC Investment Success Prediction.

Abstract

Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that rewards can possibly be exponential. Though various empirical studies and data-driven modeling work have been done, the predictive power of the complex networks of stakeholders including venture capital investors, start-ups, and start-ups' managing members has not been thoroughly explored. We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. In general, our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments and surpasses human investors by large margins. Specifically, it excels at predicting the outcomes for start-ups in industries such as healthcare and IT. Meanwhile, we shed light on impacts on start-up success from observable factors including gender, education, and networking, which can be of value for practitioners as well as policy makers when they screen ventures of high growth potentials.

Data

Prerequisites

The code has been successfully tested in the following environment. (For older PyG versions, you may need to modify the code)

• Python 3.8.12
• PyTorch 1.11.0
• Pytorch Geometric 2.0.4
• Sklearn 1.0.2
• Pandas 1.3.5

Getting Started

Prepare your data

We provide samples of our data in the ./Data folder. The input of our model is as follows:

• graph_edges includes the edges of each time step. The shape is [Time_num x 2 x Edge_num]. Time_num is the number of time steps. Edge_num is the number of the edge in this time step.
• edge_date is the time step corresponding to each edge and the length is equal to the number of all edges.
• edge_type is the edge type corresponding to each edge and the length is equal to the number of all edges.
• all_nodes is the number of nodes.
• new_companies is the index of the newly added node at each time. The shape is [(Time_num - 1) x new_add_node_length].
• labels is the label of the newly added node at each time. The shape is [(Time_num - 1 ) x new_add_node_length].
• nodetypes is the set of node types corresponding to all nodes.

Node Representation Learning

node_representation_learning.py : File for generating node representations in VC networks by node classification and link prediction tasks

python node_representation_learning.py --embedding_dim 64 --n_layers_clf 3 --train_embed --loss_type 'LPNC'

Start-up Success Prediction

startup_success_prediction.py : Code that dynamically updates newly added nodes and predicts the success of startups

python startup_success_prediction.py --dynamic_clf --gpus 'cuda:0'

File Statement Run the node_representation_learning.py file to generate the representation of the nodes and save the embedding in the file Save_model. Then run the startup_success_prediction.py file to make predictions about the success of the startups. Model/Convs.py contains MGTConvs, which is the layer to update the nodes dynamically. Predict_model in Model/Model.py is the model for startup success prediction.

Cite

Please cite our paper if you find this code useful for your research:

@misc{lyu2021graphneuralnetworkbased,
      title={Graph Neural Network Based VC Investment Success Prediction}, 
      author={Shiwei Lyu and Shuai Ling and Kaihao Guo and Haipeng Zhang and Kunpeng Zhang and Suting Hong and Qing Ke and Jinjie Gu},
      year={2021},
      eprint={2105.11537},
      archivePrefix={arXiv},
      primaryClass={cs.SI},
      url={https://arxiv.org/abs/2105.11537}, 
}