HBGSim: A structural similarity measurement over heterogeneous big graphs

Similarity measurement is fundamental to many data mining and information retrieval tasks such as link prediction and relevance-based search. Conventional similarity measurement relies more on homogenous linkage relation and content information. However, these measurements cannot take full advantage of the data structure as heterogenous graph gains increasing popularity. Moreover, the scalability of these methods also faces challenge with the never-ending growth of big data in real world. In this paper, we propose a new similarity measurement called HBGSim based on the heterogeneous structured data. HBGSim combines both local and global features by a two-stage process. We make a comparison between our measurement and some traditional methods on DBLP1 dataset for evaluation and the experimental results show that our method outperforms the others.

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