Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks (1807.03490v1)

Abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the meantime, network embedding has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. However, the heterogeneity in HINs introduces not only rich information but also potentially incompatible semantics, which poses special challenges to embedding learning in HINs. With the intention to preserve the rich yet potentially incompatible information in HIN embedding, we propose to study the problem of comprehensive transcription of heterogeneous information networks. The comprehensive transcription of HINs also provides an easy-to-use approach to unleash the power of HINs, since it requires no additional supervision, expertise, or feature engineering. To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics. To corroborate the efficacy of HEER, we conducted experiments on two large-scale real-words datasets with an edge reconstruction task and multiple case studies. Experiment results demonstrate the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneous metrics. The code and data are available at https://github.com/GentleZhu/HEER.

• The paper introduces the HEER algorithm, which utilizes edge representations to capture rich semantics in heterogeneous networks without additional supervision.
• It incorporates type-specific metrics to address semantic conflicts and achieves superior performance on benchmarks like DBLP and YAGO.
• The approach offers a scalable framework for unsupervised HIN embedding, paving the way for further research in dynamic network analysis.

An Overview of HEER: Embedding HINs Via Edge Representations

The paper "Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks" by Yu Shi et al. introduces a novel method called HEER for embedding heterogeneous information networks (HINs). HINs, which are directed graphs with diverse node and edge types, pose significant challenges due to their rich yet potentially conflicting semantics. HEER aims to address these challenges by leveraging edge representations and heterogeneous metrics.

Core Contributions and Methodology

The primary contribution of this paper is the development of the HEER algorithm, which provides a framework for embedding HINs without requiring additional supervision or feature engineering. The model innovatively uses edge representations coupled with heterogeneous metrics unique to different edge types. This coupling allows the model to handle semantic incompatibilities more effectively than existing methods. By employing this approach, HEER successfully infers and preserves diverse semantics within a unified embedding space.

The formulation of the HEER model includes:

1. Edge Representation: The method constructs edge embeddings from node embeddings, considering both directed and undirected edge properties. This is crucial for capturing the semantic richness of HINs.
2. Heterogeneous Metrics: HEER introduces edge-type-specific metrics, allowing distinct semantics and varied incompatibilities to be captured during the embedding process. The inferred metrics help mitigate the information loss typically observed when embedding nodes in a single metric space.
3. Objective Function: The model's primary objective is to reconstruct the HIN using learned embeddings by minimizing the KL divergence between observed edge weights and the type-specific closeness scores inferred from embeddings.
4. Inference Method: HEER employs mini-batch gradient descent with negative sampling for scalable training, allowing it to handle large-scale networks efficiently.

Experimental Validation and Results

The efficacy of HEER is validated through experiments on two large real-world datasets: DBLP and YAGO. The performance of HEER is evaluated against several baselines, including traditional homogeneous network embedding methods like LINE and recent heterogeneous embedding methods like AspEm and metapath2vec++. The evaluation focuses on edge reconstruction tasks where HEER demonstrates superior performance in preserving edge typology and achieving higher mean reciprocal ranks (MRR) across various knock-out rates. This performance underscores HEER’s ability to effectively capture and recreate the inherent structure and semantics of the input HIN.

Implications and Future Directions

HEER represents a significant advancement in the field of HIN embedding by effectively addressing the challenges posed by the heterogeneity and semantic complexity of real-world networks. The model's reliance on unsupervised learning procedures without the need for meta-path selection makes it highly attractive for various applications in graph mining and network analysis. Its capability to transcribe rich and incompatible information opens avenues for further research.

Future research may explore several directions based on HEER's framework, including:

• Exploration of Other Architectures: Investigating alternative methods for constructing edge embeddings and incorporating deeper network architectures could offer improved representation capabilities.
• Extension to Other Data Types: Adapting HEER's principles to cater to HINs with temporal or evolving characteristics can enrich the model's applicability.
• Integration with Higher-Order Network Structures: Incorporating motifs or graphlets might enhance HEER’s ability to capture more complex patterns inherent in HINs.

In conclusion, HEER provides a robust, versatile framework for embedding HINs and preserving their complex structure, offering a valuable tool for researchers and practitioners working with large, heterogeneous datasets.