Measuring the Relevance of Different-typed Objects in Weighted Signed Heterogeneous Information Networks

Relevance measure in both homogeneous and heterogeneous networks has been extensively studied. However, how to measure the relevance among different-typed objects in weighted signed heterogeneous information networks remains an open problem. It is challenging to incorporate both positive and negative multi-typed relationships simultaneously in signed heterogeneous networks due to the opposite opinions implied by them. To this end, this paper proposes a random walk based approach for relevance measure by utilizing and modeling the rich semantic information in weighted signed heterogeneous networks. Particularly, we first transform a signed network into a non-signed network according to the different semantic meanings represented by positive and negative relationships. This paves the way to properly utilize negative relationships. Next, we conduct random walk from the source object to the target object based on a bunch of single meta-paths separately. Finally, we combine multiple meta-paths together to obtain a more comprehensive relatedness between the source object and the target object. Extensive experiments on real datasets demonstrate the superior performance of the proposed approach.