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Tackling with these two factors at the same time is challenging. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the state-of-the-art graph contrastive learning GCL models, especially on the classification task. On graph data, the multitude of node or edge types gives rise to heterogeneous information networks HINs. UCR1 Aggregate Survey: Data for the aggregate survey are processed through a set of automated edit routines to further verify the counts for internal consistency. However, the incident-based survey allows up to four violations per incident, permitting the identification of lesser offences.