An Attention-based Graph Neural Network for Heterogeneous Structural Learning (1912.10832v1)

Abstract: 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.

Overview of "An Attention-based Graph Neural Network for Heterogeneous Structural Learning"

The paper "An Attention-based Graph Neural Network for Heterogeneous Structural Learning" introduces a novel approach to graph representation learning, targeting heterogeneous information networks (HINs). These networks are characterized by multiple types of nodes and complex relational structures, which present significant challenges over more traditional homogeneous graph structures. Previous methods often rely on meta-path-based adaptations of homogeneous graph embedding techniques, which require substantial domain expertise and can lead to suboptimal representation learning.

The authors propose the Heterogeneous Graph Structural Attention Neural Network (HetSANN), which effectively learns from the heterogeneous graph structures without resorting to meta-path schemes. This model leverages an attention mechanism directly applied to raw heterogeneous links, enabling it to capture richer structural and semantic information. HetSANN circumvents the need for domain experts to design meta-paths and automatically processes heterogeneous information, delivering more informative node representations.

Key Contributions

The primary contributions of this research can be delineated as follows:

1. HetSANN Model: The introduction of HetSANN provides a framework that directly encodes heterogeneous graph structures without meta-path interventions. This approach is facilitated by a Type-aware Attention Layer (TAL), which projects nodes into a shared low-dimensional space and employs a multi-head attention mechanism for neighborhood aggregation.
2. Innovative Extensions: The authors extend HetSANN with three enhancements:
   • Voices-sharing Product Attention: This captures the pairwise relationships in HIN by sharing attention weights between directed and reversed edges.
   • Cycle-consistency Loss: Maintains consistency in transformations across different entity spaces.
   • Multi-task Learning Integration: Utilizes auxiliary tasks to optimize node representation, enhancing the model's robustness and accuracy.
3. Empirical Evaluation: Experiments conducted on public datasets like IMDB, DBLP, and AMiner demonstrate significant improvements in node classification tasks over state-of-the-art methods. HetSANN variants exhibited superior performance, reflecting the efficacy of the model and its extensions. Remarkably, HetSANN.$M$.$R$.$V$, the full model variant, consistently achieved higher Micro F1 and Macro F1 metrics across diverse datasets and tasks.

Impact and Future Work

The practical implications of HetSANN are significant, as it alleviates the dependence on expert-crafted meta-paths, offering a more scalable and adaptable solution for analyzing HINs. This approach is particularly beneficial in domains where manual design of meta-paths is infeasible due to the complexity or dynamically evolving nature of the data.

Theoretical progress in this domain suggests potential future developments including:

• Further refinement of transformation constraints to ensure consistent representations across varying entity spaces without reliance on approximation techniques for matrix inversion.
• Expansion of HetSANN applications to other complex network structures beyond those tested, potentially influencing more areas within machine learning, such as knowledge graph learning and natural language processing tasks involving structured semantic data.
• Exploration of semi-supervised or unsupervised adaptations of HetSANN, expanding its capability in settings where labeled data is scarce.

In conclusion, HetSANN represents a robust advancement in the field of graph neural networks, providing a compelling alternative to traditional meta-path-based methods in heterogeneous graph representation learning. The results underscore its potential to advance both theoretical and practical applications in artificial intelligence.