AI / 大模型架构

Graph Neural Network Message Passing

Node feature update via neighborhood aggregation across L message-passing layers.

完整 Prompt

A graph neural network message-passing diagram.

Top — Input Graph:
- A small graph with 6 nodes and labeled edges. Each node has a feature vector x_i (drawn as a small bar above the node).

Center — Message Passing Layers (stacked, L=3 layers shown):
- For each layer:
  - Each node aggregates features from its neighbors (highlight one focal node and color its neighbors).
  - Aggregation function (mean / sum / max) shown as a small symbol on the aggregation arrow.
  - Update function (small MLP) labeled "phi" at the focal node.
  - The node feature h_i^(l) becomes h_i^(l+1).

Bottom — Readout / Pooling:
- Node features at layer L are pooled (mean or attention-weighted) into a single graph-level embedding.
- The graph embedding feeds an MLP classifier producing the prediction.

Style: clean academic vector, navy / teal palette, sans-serif labels, white background.

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适用场景

For GNN papers (GCN, GAT, GraphSAGE, MPNN) and applications like molecular property prediction.

变体

Graph Attention (GAT)

Replace simple aggregation with attention-weighted aggregation. Show the attention weights alpha_{ij} as varying-thickness edges between focal node and its neighbors.

使用建议

Highlight a single focal node per layer — showing all updates at once becomes unreadable.
Annotate aggregation type (mean / sum) explicitly. It changes the model class.
Show the readout step — without it readers don't see how graph-level predictions are made.

常见问题

Can I extend this to a heterogeneous graph?

Add per-edge-type weights and one set of node-update parameters per node type. Color edges by type to make the heterogeneity visible.