A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2011.14867v1)

Abstract: Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e.g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years. In this survey, we perform a comprehensive review of the recent development on HG embedding methods and techniques. We first introduce the basic concepts of HG and discuss the unique challenges brought by the heterogeneity for HG embedding in comparison with homogeneous graph representation learning; and then we systemically survey and categorize the state-of-the-art HG embedding methods based on the information they used in the learning process to address the challenges posed by the HG heterogeneity. In particular, for each representative HG embedding method, we provide detailed introduction and further analyze its pros and cons; meanwhile, we also explore the transformativeness and applicability of different types of HG embedding methods in the real-world industrial environments for the first time. In addition, we further present several widely deployed systems that have demonstrated the success of HG embedding techniques in resolving real-world application problems with broader impacts. To facilitate future research and applications in this area, we also summarize the open-source code, existing graph learning platforms and benchmark datasets. Finally, we explore the additional issues and challenges of HG embedding and forecast the future research directions in this field.

• The paper categorizes heterogeneous graph embedding into structure-preserved, attribute-assisted, application-oriented, and dynamic methods to tackle diverse challenges.
• It employs techniques like meta-path analysis and graph neural networks to integrate structural and attribute information effectively.
• The survey highlights real-world applications in areas such as recommendation systems and cybersecurity and outlines future research directions in self-supervised learning and dynamic modeling.

Overview of Heterogeneous Graph Embedding Approaches

The survey titled "A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources" provides a detailed exploration of the advances in the domain of heterogeneous graph (HG) embedding. The paper meticulously categorizes the methodologies, techniques, applications, and resources surrounding the embedding of heterogeneous graphs, which differ from homogeneous graphs by containing diverse node and link types. These heterogeneities pose unique challenges that have fueled a surge in research aimed at developing effective embedding strategies for HGs.

Methodological Classifications

This survey cleverly segments heterogeneous graph embedding methods into four principal categories: structure-preserved HG embedding, attribute-assisted HG embedding, application-oriented HG embedding, and dynamic HG embedding. Each category is distinguished by the unique types of information leveraged during the embedding process.

1. Structure-Preserved HG Embedding focuses on preserving HG's complex structures, utilizing meta-paths, subgraphs, and distinct link types to embody the semantic relations. Notable methods in this category often employ random walk-based approaches or rely on hybrid relation modeling to achieve contextual learning.
2. Attribute-Assisted HG Embedding combines structural data with node attributes, leaning on graph neural networks (GNNs) to evaluate heterogeneous characteristics effectively. This results in embedding techniques that are both inductive and capable of understanding the variance in node and edge types.
3. Application-Oriented HG Embedding is tailored for specific applications by intelligently incorporating domain knowledge and application-specific features, such as those used in recommendation systems and user identification tasks. This approach yields embeddings that extend beyond general-purpose uses to enable practical, real-world applications.
4. Dynamic HG Embedding addresses the temporal nature of many real-world heterogeneous graphs. Methods like DyHNE use matrix perturbation theory to incrementally update node embeddings, considering the evolutionary nature of data over time.

Implications and Applications

The practical implications of heterogeneous graph embedding are far-reaching across various domains. In e-commerce, HG embedding facilitates sophisticated recommendation systems and user profiling, while in cybersecurity, it aids in malware detection and identifying malicious users in networks. The adoption of HG embedding in these fields underscores its ability to model complex and multifaceted relationships, providing nuanced insights that drive effective decision-making and risk management strategies.

Challenges and Future Directions

Despite significant progress, several challenges persist in the field of heterogeneous graph embedding. Methodological challenges include selecting optimal meta-paths and efficiently fusing structure and attribute information. Additionally, the need for scalable and explainable models remains critical, especially given the large-scale nature of industrial data.

The survey hints at promising future research directions, such as the development of self-supervised and pre-training techniques tailored to HGs, exploration of hyperbolic embedding spaces, and robust handling of dynamic graph data. Furthermore, augmenting HG embedding methods with fairness, robustness, and interpretability is critical, especially in sensitive application areas like cybersecurity and healthcare.

Overall, this survey serves as a valuable resource for researchers seeking to navigate the intricate landscape of heterogeneous graph embedding, illuminating both the technical underpinnings and practical applications of this rapidly evolving field. As heterogeneous data becomes increasingly prevalent, the continuous evolution of HG embedding techniques will play a crucial role in harnessing its full potential.