# Temporal graph neural networks

Splitting the Enron email network into 44 thirty-day snapshots and matching maximal cliques one-to-one by Jaccard similarity leaves about 60% of cliques without a successor, so next-month tension is undefined for most rows. Over 24 rolling origins, residual GCN, GraphSAGE-style and GCN+GRU models that start at persistence are equivalent to carrying the current value forward on surviving cliques, a plain GCN does worse, and neighbourhood and recurrent memory add nothing measurable. In the deployable setting, multiplying persistence by a simple hazard-model survival probability lowers error by about 17%.

Splitting the Enron email network into 44 thirty-day snapshots and matching maximal cliques one-to-one by Jaccard similarity leaves about 60% of cliques without a successor, so next-month tension is undefined for most rows. Over 24 rolling origins, residual GCN, GraphSAGE-style and GCN+GRU models that start at persistence are equivalent to carrying the current value forward on surviving cliques, a plain GCN does worse, and neighbourhood and recurrent memory add nothing measurable. In the deployable setting, multiplying persistence by a simple hazard-model survival probability lowers error by about 17%.