HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks (1309.7393v1)

Abstract: Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However, in many scenarios, we need to measure the relatedness between objects with different types. With the surge of study on heterogeneous networks, the relevance measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). A novel measure HeteSim is proposed, which has the following attributes: (1) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (2) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connect two objects through following a sequence of node types; (3) a semi-metric measure: HeteSim has some good properties (e.g., self-maximum and symmetric), that are crucial to many data mining tasks. Moreover, we analyze the computation characteristics of HeteSim and propose the corresponding quick computation strategies. Empirical studies show that HeteSim can effectively and efficiently evaluate the relatedness of heterogeneous objects.

• The paper introduces HeteSim, a novel metric that uniformly measures relatedness between diverse network objects using path constraints.
• HeteSim employs a path-constrained approach with symmetric and semi-metric properties, enhancing profiling, recommendation, and ranking tasks.
• Empirical results validate HeteSim’s superior performance over traditional similarity measures, demonstrating its efficacy in complex network analysis.

An Analysis of "HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks"

Overview

The paper introduces HeteSim, a novel metric for evaluating the relevance or relatedness between objects in heterogeneous information networks (HINs). Unlike traditional similarity measures, HeteSim is unique in its ability to assess the relatedness of both same-type and different-type objects within a unified framework constrained by specific paths. The measure is motivated by the increasing complexity and interdisciplinary nature of network-based data, which involves various types of nodes and links, such as authors, papers, and conferences in academic networks or actors, movies, and directors in entertainment networks.

Contributions

This work contributes a methodologically robust approach to tackling the relevance measure problem, addressing some noted challenges:

1. Uniform Framework: HeteSim provides a uniform approach for computing the relatedness of objects, allowing for comparisons across heterogeneous types via a consistent metric.
2. Path-Constrained Measure: Relatedness is determined by a path in the network - a sequence of node types, capturing the semantics of the relationships embodied in these pathways.
3. Symmetric and Semi-Metric Properties: HeteSim maintains desirable properties like symmetry and non-negativity, extending its applicability to various data mining tasks such as clustering and collaborative filtering.
4. Computation Strategy: The paper details computation characteristics and proposes efficient strategies for calculating HeteSim, significantly mitigating the computational overhead typically associated with similarity metrics in complex networks.
5. Empirical Validation: Through exhaustive experiments and case studies across various datasets, HeteSim’s efficacy is demonstrated, showing its capability to effectively capture the nuanced semantics of heterogeneous objects and paths.

Empirical Results and Implications

The paper's empirical analysis includes applications such as automatized profiling, expert finding, relevance search, and recommendation systems. The results underscore the effective capture of path-specific semantics and highlight HeteSim’s superior performance over existing methods like SimRank and PCRW, especially noteworthy for tasks requiring cross-type relevance assessments.

• HeteSim was demonstrated in the domain of academic profiling, revealing intricacies such as the domains most relevant to a researcher based on their publication history, a task where traditional homogenous similarity measures fall short.
• The expert-finding task showed HeteSim's ability to establish relative importance among objects, an aspect crucial for accurately ranking and recommending entities.

Future Directions

The research opens several avenues for expanding HeteSim’s applicability and efficacy:

• Meta Path Selection: A more autonomous mechanism for selecting relevant paths could be integrated to enhance flexibility and scalability in diverse application contexts.
• Distributed Computation: Exploring distributed computing environments for HeteSim to handle scalability issues in large-scale network data is warranted.
• Theoretical Expansion: Further exploration into the metric properties of HeteSim could yield theoretical insights into its application in broader contexts beyond currently considered relational structures.

Conclusion

In conclusion, the HeteSim framework represents a significant advancement in relevance measurement techniques within heterogeneous networks. By integrating the capacity to assess cross-type object relatedness in a symmetric, path-constrained, and uniform manner, this approach extends the analytical toolkit available to researchers grappling with the complexities inherent in networked data systems. The experimental results attest to HeteSim's robustness and versatility, offering practical and theoretical implications for future research and development in the paper and application of heterogeneous information networks.