Abstract:
HINs are used to model information systems with multi-type objects and relations. Homogeneous graphs have one type of nodes and edges.
Web search, link prediction, and clustering require measuring object similarities. HIN similarity measures exist. Most of these measures use meta-paths, which show node class and edge type sequences between two nodes.
Meta-paths, often designed by domain experts, are difficult to enumerate and choose based on similarity scores. This makes applying similarity measures in practice difficult. We introduce the decay graph to extend SimRank, a well-known similarity measure on homogeneous graphs, to HINs.
HowSim, a new similarity measure, is meta-path-free and captures structural and semantic similarity simultaneously. Extensive experiments show HowSim’s generality, effectiveness, and efficiency.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
