Top-k Similarity Join in Heterogeneous Information Networks
Abstract— Top-k Similarity Join in Heterogeneous Information Networks. As a newly emerging network model, heterogeneous information networks < Final Year Projects 2016 > have received growing attention. Many data mining tasks have been explored in HINs, including clustering, classiﬁcation, and similarity search. Similarity join is a fundamental operation required for many problems. It is attracting attention from various applications on network data, such as friend recommendation, link prediction, and online advertising. Although similarity join has been well studied in homogeneous networks, it has not yet been studied in heterogeneous networks. Especially, none of the existing research on similarity join takes different semantic meanings behind paths into consideration and almost all completely ignore the heterogeneity and diversity of the HINs.