arXiv Machine Learning By Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri

Efficient Recommendations via Graph Coarsening and Label Propagation

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arXiv:2607. 22287v1 Announce Type: new Abstract: Graph-based recommendations are widely adopted in real-world industrial applications.

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arXiv Machine Learning
Jun 10

When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice

arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.

By Neha Sharma, Ritesh Sharma