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This paper extends the evaluation of the SNAP-KG framework to heterophilous graphs, showing that homophily in at least one view is crucial for performance in streaming entity integration, highlighting a shared assumption in multi-view graph clustering methods.
SNAP-KG is a framework that enables efficient integration of streaming entities into knowledge graphs via multi-view clustering and inductive inference, reducing inference time and candidate search space for downstream tasks like entity resolution and link prediction.