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.

本篇论文研究了异构网络中的相关搜索问题，提出了一种名为 HeteSim 的度量方法，它具有均匀、路径约束、半度量等特点，并提出了相应的计算策略，实验表明，HeteSim 能够有效、高效地评估异构对象之间的相关性。