A Survey of Heterogeneous Information Network Analysis (1511.04854v1)

Abstract: Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

• The paper presents an exhaustive review of HIN methodologies, emphasizing key techniques like meta paths and network schema for enhanced data mining.
• It contrasts HINs with multi-relational and complex networks to demonstrate the unique advantages of modeling diverse object types and relationships.
• The survey outlines practical applications and future research directions, focusing on scalability and advanced methods for mining big, heterogeneous data.

A Survey of Heterogeneous Information Network Analysis

The paper entitled "A Survey of Heterogeneous Information Network Analysis" by Shi et al. provides an exhaustive examination of the developments and challenges in the analysis of heterogeneous information networks (HINs). HINs offer a more granular representation of real-world systems by acknowledging the diversity in object types and relationships which many contemporary studies often overlook when using homogeneous networks. This survey paper synthesizes current research, outlines foundational concepts, and proposes directions for future investigation.

Core Concepts

HINs pivot on the differentiation of object and link types, which introduces complexity and semantic richness beyond that found in homogeneous networks. Basic definitions include the concepts of information network and its subclass heterogeneous information network, differing fundamentally in the variety of object and link types they contain. The network schema provides a meta-level description while meta paths capture the routes through which nodes are interconnected, yielding diverse physical meanings.

Distinction from Related Concepts

The paper juxtaposes HINs against related concepts like multi-relational networks, multi-dimensional/mode networks, composite networks, and complex networks. Multi-relational and multi-dimensional networks are special cases of HINs with restrictions in object variety, while HINs allow for comprehensive, multi-typed interactions. Similarly, unlike composite networks and complex networks, HINs explicitly structure the semantic relationships among diverse object types, paving the way for enhanced analysis and data mining.

Applications in Data Mining Tasks

Shi et al. categorize over 100 papers into key data mining tasks, demonstrating the breadth of applications and the unique advantages brought by HINs:

1. Similarity Measure: PathSim and HeteSim are notable methods leveraging meta paths to measure similarities. These measures highlight the importance of context and semantic paths in determining similarities, offering results that align more closely with real-world semantic relationships.
2. Clustering: Unlike traditional clustering that relies solely on homogeneous links, clustering in HINs integrates multi-typed and semantically rich data, offering granular insights into network substructures. Methods like NetClus and RankClus exemplify this by clustering entities alongside ranking them, capturing richer interrelations.
3. Classification: HIN-aware classifiers like GNetMine and HetPathMine use the structured heterogeneity of data to enhance classification accuracy. They illustrate the utility of HINs in modeling complex dependencies among multiple object types for improved label prediction.
4. Link Prediction: The use of meta path-based features and probabilistic models facilitates the prediction of multi-typed links within HINs. Methods such as PathPredict and MRIP underscore the efficacy of heterogeneous relations in enhancing prediction accuracy.
5. Ranking: Co-ranking and path-based ranking models highlight the potential of HINs in discerning object importance through diverse semantic paths. Methods like HRank demonstrate the multi-faceted nature of importance in heterogeneous contexts.
6. Recommendation: Systems like HeteRecom and SemRec leverage meta paths to improve recommendation quality by incorporating semantic information of interactions, showcasing the practical benefits of HINs in everyday applications like movie recommendations.
7. Information Fusion: Network alignment and subgraph isomorphism play crucial roles in integrating information from multiple HINs, addressing challenges arising from partially aligned networks and schema complexities.

Future Directions

HINs pose unique research challenges and opportunities:

• Complex Network Construction: The need for enhanced cleaning and integration techniques for structured and unstructured data is paramount. This may include entity resolution, relationship extraction, and attribute incorporation.
• Advanced Mining Methods: As real-world networks become more intricate, developing methods for subtle semantic capture, such as constrained meta paths and weighted links, becomes essential.
• Bigger Networked Data: Scaling up algorithms for big data environments using parallel computation and effective partitioning strategies is crucial for practical applications.
• Broader Applications: Extending HIN analysis to underexplored areas like OLAP and information diffusion could unlock new avenues for research and practical insights.

Conclusion

The detailed survey by Shi et al. encapsulates the expansive potential of heterogeneous information networks to revolutionize traditional data mining tasks by leveraging the complex and rich semantics embedded in real-world data. The insights provided pave the way for future theoretical advancements and practical applications, making it a vital reference for researchers in the field of data mining and network analysis.