Abstract: The emerging of large-scale interconnection networks such as social networks and bibliographic networks raised many challenges for similarity search, wherein the similarity measure is one of the key issues. The existing methods of similarity measure did not consider the different semantic of multiple paths on homogeneous networks. This paper proposes a novel similarity measure method based on meta path, which can search the same type of objects on heterogeneous networks. Meta path is path consisting of a series of relationships defined in the different object types, which provides a common basis for the similarity search engines on the networks. Experiments on real data sets show that compared to the disorder similarity measures, the proposed method supports fast path similarity query, and it can be widely used in social networks and e-commerce.

Key words: similarity search, social networks, meta path, heterogeneous network