Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups
Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups
复制标题
DOI：
10.1145/3447548.3467383
复制
发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Shengming Zhang;Hao Zhong;Zixuan Yuan;Hui Xiong
中科院分区：
文献类型：
--
作者：
Shengming Zhang;Hao Zhong;Zixuan Yuan;Hui Xiong
对于风险投资者来说，在早期阶段发现高潜力的创业公司至关重要。事实上，许多努力已经通过拓扑分析的异构信息网络的人，启动，风险投资公司或潜在的启动配置文件功能的表示学习的创业公司成功的关键因素进行了研究。然而，现有的拓扑分析缺乏对异构信息的深入理解。此外，基于表示学习的方法在很大程度上依赖于特定领域的知识进行特征选择。相反，在本文中，我们提出了一个可扩展的异构图马尔可夫神经网络（SHGMNN），用于识别高潜力的创业公司。总体思路是使用图神经网络（GNN）通过端到端的有效训练来学习有效的创业表示，并通过最大后验（MAP）推理来建模创业公司之间的标签依赖关系。具体来说，我们首先定义不同的元路径，以捕捉各种语义的异构信息网络（HIN）和聚合所有的语义信息到一个求和图结构。为了预测高潜力的早期创业公司，我们引入了GNN来将信息扩散到求和图上。然后，我们采用铰链损失马尔可夫随机场上的MAP推理来强制标签依赖性。这里，引入了伪似然变分期望最大化（EM）框架来迭代优化MAP推理和GNN：E步骤计算推理，M步骤更新GNN。为了提高效率，我们开发了一个轻量级的线性扩散架构的GNN执行网络规模的异构信息网络的图形传播。最后，在真实数据集上的大量实验和案例研究证明了SHGMNN的优越性。
It is critical and important for venture investors to find high-potential startups at their early stages. Indeed, many efforts have been made to study the key factors for the success of startups through the topological analysis of the heterogeneous information network of people, startup, and venture firms or representation learning of latent startup profile features. However, the existing topological analysis lacks an in-depth understanding of heterogeneous information. Also, the approach based on representation learning heavily relies on domain-specific knowledge for feature selections. Instead, in this paper, we propose aScalable Heterogeneous Graph Markov Neural Network (SHGMNN) for identifying the high-potential startups. The general idea is to use graph neural networks (GNN) to learn effective startup representations through end-to-end efficient training and model the label dependency among startups through Maximum A Posterior (MAP) inference. Specifically, we first define different metapaths to capture various semantics over the heterogeneous information network (HIN) and aggregate all semantic information into a summated graph structure. To predict the high-potential early-stage startups, we introduce GNN to diffuse the information over the summated graph. We then adopt an MAP inference over Hinge-Loss Markov Random Fields to enforce label dependency. Here, a pseudolikelihood variational expectation-maximization (EM) framework is incorporated to optimize both MAP inference and GNN iteratively: The E-step calculates the inference, and the M-step updates the GNN. For efficiency concerns, we develop a GNN with a lightweight linear diffusion architecture to perform graph propagation over web-scale heterogeneous information networks. Finally, extensive experiments and case studies on real-world datasets demonstrate the superiority of SHGMNN.