论文标题
图混合物密度网络
Graph Mixture Density Networks
论文作者
论文摘要
我们介绍了图形混合物密度网络，这是一个新的机器学习模型系列，可以符合以任意拓扑图为条件的多模式输出分布。通过结合混合模型和图形表示学习的想法，我们解决了依赖结构化数据的一系列更广泛的有条件密度估计问题。在这方面，我们在一个新的基准应用程序上评估了我们的方法，该应用利用随机图进行随机图。考虑到多模式和结构，我们显示出流行性结果的可能性有显着改善。经验分析通过两个现实世界回归任务进行补充，该任务表明我们方法在建模输出预测不确定性方面的有效性。图形混合物密度网络在研究表现出非平凡的条件输出分布的结构依赖现象的研究中开放了吸引力的研究机会。
We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. By combining ideas from mixture models and graph representation learning, we address a broader class of challenging conditional density estimation problems that rely on structured data. In this respect, we evaluate our method on a new benchmark application that leverages random graphs for stochastic epidemic simulations. We show a significant improvement in the likelihood of epidemic outcomes when taking into account both multimodality and structure. The empirical analysis is complemented by two real-world regression tasks showing the effectiveness of our approach in modeling the output prediction uncertainty. Graph Mixture Density Networks open appealing research opportunities in the study of structure-dependent phenomena that exhibit non-trivial conditional output distributions.
