Abstract:
Heterogeneous information networks (HINs)—typed graphs with labeled nodes and edges—are popular in academia and industry.
We discover the k most important meta paths in real time for friend search, product recommendation, anomaly detection, and graph clustering using two HIN nodes s and t and a natural number k. We propose that the shortest path between s and t may not be the best.
Thus, we redefine the meta path importance function between s and t by combining several ranking functions based on frequency and rarity. This importance function captures more information, but finding top-k meta paths is time-consuming.
Thus, we use this importance function in a multi-step framework to efficiently filter impossible meta paths between s and t. This framework also uses bidirectional searching algorithm to improve efficiency. Our method outperforms state-of-the-art algorithms with reasonable response time on multiple datasets.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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