GraphHINGE：异构信息网络上结构化邻域的学习交互模型
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
摘要：
异构信息网络（HIN）已被广泛用于描述各种类型的实体及其复杂关系。最近的尝试要么依赖于显式路径可达性，以利用基于路径的语义相关性或图形邻域来学习异构网络表示预测之前。这些弱耦合方式忽略了邻居节点之间的丰富交互，这引入了早期总结问题。在本文中，我们提出了GraphHINGE（异构交互和aggregatE），它通过结构化的邻域捕获和聚合每对节点之间的交互模式。具体来说，我们首先引入基于邻居的交互（NI）模块来建模相同元路径下的交互模式，然后将其扩展为跨邻居的交互（CNI）模块来处理不同元路径下的交互模式。接下来，为了解决大规模网络的复杂性问题，我们通过卷积框架制定交互模块，并通过快速傅立叶变换有效地学习参数。此外，我们设计了一种新的基于邻域的选择（NS）机制，采样策略，过滤高阶邻域信息的基础上，他们的低阶性能。在六种不同类型的异构图上进行了大量的实验，通过与最先进的点击率预测和top-N推荐任务进行比较，证明了性能的提高。
英文摘要：
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook the rich interactions among neighbor nodes, which introduces an early summarization issue. In this paper, we propose GraphHINGE (Heterogeneous INteract and aggreGatE), which captures and aggregates the interactive patterns between each pair of nodes through their structured neighborhoods. Specifically, we first introduce Neighborhood-based Interaction (NI) module to model the interactive patterns under the same metapaths, and then extend it to Cross Neighborhood-based Interaction (CNI) module to deal with different metapaths. Next, in order to address the complexity issue on large-scale networks, we formulate the interaction modules via a convolutional framework and learn the parameters efficiently with fast Fourier transform. Furthermore, we design a novel neighborhood-based selection (NS) mechanism, a sampling strategy, to filter high-order neighborhood information based on their low-order performance. The extensive experiments on six different types of heterogeneous graphs demonstrate the performance gains by comparing with state-of-the-arts in both click-through rate prediction and top-N recommendation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
