基于异构匿名行走的异构结构表示学习
Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
摘要：
结构相似性的捕获对于理解节点的功能和行为有很大的帮助，是近年来网络嵌入研究领域的一个热点。然而，已有的研究工作主要关注同构网络上的学习结构，而异构网络上的相关研究还很缺乏。在本文中，我们试图采取的第一步，表示学习的异质结构，这是非常具有挑战性的，由于其高度多样化的组合的节点类型和底层结构。为了有效地区分不同的异质结构，我们首先提出了一种理论上有保证的技术称为异质匿名行走（HAW）及其变体粗HAW（CHAW）。然后，我们设计了异构匿名步行嵌入（HAWE）和它的变种粗HAWE在数据驱动的方式，以规避使用非常大量的可能的步行和训练嵌入预测发生在每个节点的邻域中的步行。最后，我们设计和应用广泛的和说明性的实验合成和现实世界的网络上建立一个基准的异质结构学习和评估我们的方法的有效性。实验结果表明，与同类和异构网络的经典方法相比，本文方法具有优异的性能，可以应用于大规模网络。
英文摘要：
Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks is still a void. In this paper, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we firstly propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and its variant coarse HAW (CHAW). Then, we devise the heterogeneous anonymous walk embedding (HAWE) and its variant coarse HAWE in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods, and can be applied on large-scale networks.
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