HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
By Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu. IEEE Transactions on Knowledge and Data Engineering, 2013
329 citations recorded by the source corpus.
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 connects two objects through following a sequence of node types; (3) a semi-metric measure: HeteSim has some good properties (e.g., selfmaximum and symmetric), which 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.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/bfc39ee1e7100341df2c0372c23efcdeab7cbe48
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Citations in the corpus
- Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (2018, background): “As a newly emerging direction, heterogeneous information network [28] can naturally model complex objects and their rich relations in recommender systems, in which objects are of different types and links among objects represent different relations [27, 30].”
- A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, unknown): “Previous work on medical HG utilizes HeteSim [107] to analyze the similarities between objects [135].”
- Heterogeneous Information Network Embedding for Recommendation (2017, background): “And several path based similarity measures [4], [33], [34] are proposed to evaluate the similarity of objects in heterogeneous information network.”