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

arXiv: https://arxiv.org/abs/2105.12918 · HTML: https://mlsanity.com/paper/2105.12918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 24 of 24)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Memory Augmented Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1912.11730/) (2019)
- [MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network](https://mlsanity.com/paper/1905.11013/) (2019)
- [Graph Enhanced Representation Learning for News Recommendation](https://mlsanity.com/paper/2003.14292/) (2020)
- [Context-Aware Visual Compatibility Prediction](https://mlsanity.com/paper/1902.03646/) (2019)
- [Contextual LSTM (CLSTM) models for Large scale NLP tasks](https://mlsanity.com/paper/1602.06291/) (2016)
- [Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification](https://mlsanity.com/paper/1904.04238/) (2019)
- [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](https://mlsanity.com/paper/1712.04443/) (2017)
- [A Quantum-inspired Similarity Measure for the Analysis of Complete Weighted Graphs](https://mlsanity.com/paper/1904.13239/) (2019)
- [Learning the Implicit Semantic Representation on Graph-Structured Data](https://mlsanity.com/paper/2101.06471/) (2021)
- [Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis](https://mlsanity.com/paper/1910.09153/) (2019)
- [Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model](https://mlsanity.com/paper/1912.06767/) (2019)
- [Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach](https://mlsanity.com/paper/1912.12016/) (2019)

## Built on by (most cited first, 2 of 2)

- [Using Deep Learning to Find the Next Unicorn: A Practical Synthesis](https://mlsanity.com/paper/2210.14195/) (2022)
- [Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction](https://mlsanity.com/paper/2204.08229/) (2022)

## Abstract (arXiv)

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.