In this paper, we focus on graph representation learning of heterogeneous
information network (HIN), in which various types of vertices are connected by
various types of relations. Most of the existing methods conducted on HIN
revise homogeneous graph embedding models via meta-paths to learn
low-dimensional vector space of HIN. In this paper, we propose a novel
Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly
encode structural information of HIN without meta-path and achieve more
informative representations. With this method, domain experts will not be
needed to design meta-path schemes and the heterogeneous information can be
processed automatically by our proposed model. Specifically, we implicitly
represent heterogeneous information using the following two methods: 1) we
model the transformation between heterogeneous vertices through a projection in
low-dimensional entity spaces; 2) afterwards, we apply the graph neural network
to aggregate multi-relational information of projected neighborhood by means of
attention mechanism. We also present three extensions of HetSANN, i.e.,
voices-sharing product attention for the pairwise relationships in HIN,
cycle-consistency loss to retain the transformation between heterogeneous
entity spaces, and multi-task learning with full use of information. The
experiments conducted on three public datasets demonstrate that our proposed
models achieve significant and consistent improvements compared to
state-of-the-art solutions.

本文针对异构信息网络（HIN）的图表示学习进行了研究，并提出了一种新颖的基于异构图结构注意力神经网络（HetSANN）的方法，该方法在不需要领域专家设计元路径方案的情况下能够自动处理异构信息。实验证明，该方法相较于现有的 HIN 嵌入模型有显著和一致的提升。

基于注意力机制的异构结构学习图神经网络

An Attention-based Graph Neural Network for Heterogeneous Structural  Learning

Events are happening in real-world and real-time, which can be planned and
organized occasions involving multiple people and objects. Social media
platforms publish a lot of text messages containing public events with
comprehensive topics. However, mining social events is challenging due to the
heterogeneous event elements in texts and explicit and implicit social network
structures. In this paper, we design an event meta-schema to characterize the
semantic relatedness of social events and build an event-based heterogeneous
information network (HIN) integrating information from external knowledge base,
and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN)
based fine-grained social event categorization model. We propose a
Knowledgeable meta-paths Instances based social Event Similarity (KIES) between
events and build a weighted adjacent matrix as input to the PP-GCN model.
Comprehensive experiments on real data collections are conducted to compare
various social event detection and clustering tasks. Experimental results
demonstrate that our proposed framework outperforms other alternative social
event categorization techniques.

本文提出了一个事件元结构来表征社交事件的语义相关性并构建了一个基于事件的异构信息网络（HIN），该网络合并了外部知识库中的信息，并提出了一种新的基于 KIES 的 PP-GCN 模型，用于对社交事件进行分类。实验结果表明，该方法在社交事件分类方面优于其他替代方法。