论文标题
异质图稀疏以进行有效表示学习
Heterogeneous Graph Sparsification for Efficient Representation Learning
论文作者
论文摘要
图形稀释剂是近似任意图的强大工具，并已用于均匀图的机器学习中。但是，在诸如知识图之类的异质图中，尚未系统利用稀疏以提高学习任务的效率。在这项工作中，我们启动了有关异质图稀疏性的研究，并开发了基于采样的算法，用于构造稀疏器，这些散布器可证明是稀疏并保留原始图中重要信息的。我们已经进行了广泛的实验，以确认所提出的方法可以改善表示形式学习的时间和空间复杂性，同时实现可比性，甚至可以根据学习的嵌入在后续的图形学习任务中更好地表现。
Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs. In heterogeneous graphs such as knowledge graphs, however, sparsification has not been systematically exploited to improve efficiency of learning tasks. In this work, we initiate the study on heterogeneous graph sparsification and develop sampling-based algorithms for constructing sparsifiers that are provably sparse and preserve important information in the original graphs. We have performed extensive experiments to confirm that the proposed method can improve time and space complexities of representation learning while achieving comparable, or even better performance in subsequent graph learning tasks based on the learned embedding.
