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Reseau neuronal
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Reseau neuronal graphique
Graph neural networks learn on graph-structured data by exchanging and aggregating information across edges.
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Résumé
Message-passing depth limits how many graph hops can influence a node in one forward pass; model behavior depends on architecture, graph and task.
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In an L-layer message-passing GNN, information can propagate at most L graph hops, since each layer aggregates from immediate neighbors. Over-smoothing describes a failure mode in which repeated propagation makes node embeddings increasingly similar; it is architecture- and setting-dependent, not an inevitable convergence of every GNN. GraphSAGE learns aggregation functions for unseen nodes, while the original GCN uses symmetrically degree-normalized adjacency propagation. Molecular message passing represents atoms and bonds as graph nodes and edges.
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The molecular message-passing paper represents atoms as nodes and bonds as edges for molecular property prediction.
GraphSAGE studies inductive node representations that can be computed for unseen nodes using neighborhood features.
GCN and GAT papers provide distinct examples of degree-normalized propagation and learned neighbor attention.
Deep message passing can produce over-smoothing in some architectures, where node representations become less distinguishable.
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These cited papers establish specific graph-learning methods and evaluated tasks. They do not establish that GNNs outperform other model families broadly; any comparison should name the dataset, task and baseline.
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What is Graph Neural Networks?
Graph neural networks learn on graph-structured data by exchanging and aggregating information across edges. Message-passing depth limits how many graph hops can influence a node in one forward pass; model behavior depends on architecture, graph and task.
Lan mooy jumtukaayu ordinatër bu mag bi ci reso neuronal yu bari yi?
GNN yi dañuy liggéey ci fexe ba node bu nekk dajale ay mesaas yu bawoo ci dëkkandoo yi, ba noppi yeesal représentation boppam, baamtu ko ci diisaay yi.
Lan moo waral fonction agrégation dekkandoo bu GNN wara nekk permutation budul soppiku?
Ginaaw amul benn yoon buñ tëral ci dëkkandoo yi ci node bi, ay jëf yu melni sum, moyenne, wala max dañuy fexe ba génne gi nekkul ci ni ñu lim dëkkandoo yi.
Lan mooy 'yootu lu ëpp' ci GNN yu xóot yi?
Over-smoothing is a possible failure mode in some deep message-passing GNNs: node embeddings can become increasingly similar as layers are stacked. It is not inevitable for every architecture, graph, or task.
How does a Graph Attention Network differ from a basic GCN layer?
GAT learns attention weights over neighbors. The standard GCN uses a degree-normalized adjacency propagation rule rather than uniform unnormalized weights.
Ci GNN buñu def ci benn molecule, lan la node yi ak boor yi di màndargaal?
The cited molecular message-passing paper represents atoms as nodes and bonds as edges; this is an application example, not a broad claim that GNNs outperform other methods.
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