用图神经网络分析企业、课题和机构关系，精准筛选有潜力的中小企业。
Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks
- 构建企业-课题-机构异构图，用Transformer模型捕捉多类型关系。
- 在100家筛查深度下精度达89.6%，比随机选择提升2.14倍。
- 仅用公开数据，结果可复现，适合政策制定者和早期投资者参考。
中小型企业（SME）占美国企业总数的99.9%，贡献44%的经济活动，但系统性识别高潜力企业仍是难题。我们提出SME-HGT，一种异构图变压器框架，仅使用公开数据预测哪些小企业创新研究计划（SBIR）一期获奖者将进入二期资助。构建包含32,268个公司节点、124个研究主题节点和13个政府机构节点的异构图，通过约9.9万条边连接，涵盖三种语义关系类型。SME-HGT在时间划分测试集上取得0.621±0.003的AUPRC，优于MLP基线（0.590±0.002）和R-GCN（0.608±0.013），在五个随机种子下均表现更优。在筛选前100家企业时，精确率达到89.6%，相比随机选择提升2.14倍。时间划分评估协议防止信息泄露，依赖公开数据确保结果可复现。结果表明，企业、研究主题与资助机构之间的关联结构蕴含显著的潜力信号，对政策制定者和早期投资者具有重要意义。
原文摘要 · Abstract (English)
Small and Medium Enterprises (SMEs) constitute 99.9% of U.S. businesses and generate 44% of economic activity, yet systematically identifying high-potential SMEs remains an open challenge. We introduce SME-HGT, a Heterogeneous Graph Transformer framework that predicts which SBIR Phase I awardees will advance to Phase II funding using exclusively public data. We construct a heterogeneous graph with 32,268 company nodes, 124 research topic nodes, and 13 government agency nodes connected by approximately 99,000 edges across three semantic relation types. SME-HGT achieves an AUPRC of 0.621 0.003 on a temporally-split test set, outperforming an MLP baseline (0.590 0.002) and R-GCN (0.608 0.013) across five random seeds. At a screening depth of 100 companies, SME-HGT attains 89.6% precision with a 2.14 lift over random selection. Our temporal evaluation protocol prevents information leakage, and our reliance on public data ensures reproducibility. These results demonstrate that relational structure among firms, research topics, and funding agencies provides meaningful signal for SME potential assessment, with implications for policymakers and early-stage investors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。