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Found 2 papers, 1 papers with code
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
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