TSS: Temporal similarity search measure for heterogeneous information networks.

Many real-world phenomena can be modeled as networked systems. Some of these systems consist of heterogeneous nodes and edges. Similarity search is a fundamental operation in network systems, which is a basis for various applications such as link prediction and recommendation. This manuscript introduces a temporal similarity measure for heterogeneous networks. The proposed metric is based on metapath strategy and considers time of the interaction. We provide detailed properties of the proposed temporal similarity measures. Our experimental results on a number of real heterogeneous social networks show that incorporating time in the computation of the similarity significantly improves the performance as compared to the state-of-the-art similarity computation methods. • We propose a temporal similarity measure for heterogeneous information networks. • The proposed metric is based on metapath strategy and considers time of the interaction. • Experiments represent the effectiveness of our measure, in comparison with the state-of- the-art similarity computation methods. [ABSTRACT FROM AUTHOR]