MGT Model

This is the source code for paper Help Me Screen: Analyzing and Predicting the Success of Start-ups in Dynamic Venture Capital Networks.

Abstract

Most start-ups fail, and early-stage ventures face even lower survival rates. Identifying high-potential start-ups remains a critical challenge for venture capital (VC) investors and policymakers. While predictive models exist, the evolving relationships between VC investors, start-ups, and management teams in dynamic networks are underexplored. We propose a method to predict whether a start-up will succeed within five years of its first funding round. Using a 40-year global VC dataset, we model the VC ecosystem as a dynamic bipartite network linking start-ups to individuals (investors/managers). Our approach incrementally updates graph embeddings through unsupervised self-attention to incorporate new nodes, edges, and their neighbors. Node embeddings are further fine-tuned via link prediction and classification tasks, while temporal dependencies are captured to form sequential representations. The model identifies early-stage start-ups with twice the success likelihood of those chosen by professional investors. Key factors including networking and education align with VC literature. Additionally, we provide model complexity analysis and open-source our implementation to support practical applications and future research.

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 True --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:

citation