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

HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks