Meta Path Based Top K Similarity Join In Heterogeneous Information Networks
Heterogeneous information networks (HINs) has received growing attention in a newly emerging network model. Clustering, classification and similarity search are the data mining tasks explored in HINs. Similarity join operation is required for many problems which is attracting attention from various applications on network data which includes friend recommendation, link prediction and online advertising. Similarity join is studied in homogeneous network but not studied in heterogeneous networks. The heterogeneity of the HINs are ignored completely by the previous research on similarity join which takes different semantic meanings. A Meta Path based Similarity join (MPS join) method to return the top k similar pairs of objects based on the user specified join path in a HIN is proposed and to prune expensive similarity computation by using BPLSH (Bucket Pruning based Locality Sensitive Hashing) indexing. When compared to the existing Link based Similarity join (LS join) method, this MPSjoin method derives various similarity semantics. The experimental results on real data sets shows the efficiency and effectiveness of the proposed approach.