How deeply does Knowledge Graphs and LLMs in Action cover residual connection?

Explains shortcut motivation and a concatenation-based update, while deferring detailed residual variants and broader architecture design.

Residual connection

As explained in Knowledge Graphs and LLMs in Action

A residual connection is a neural-network connection that combines a layer's input with its transformed output to facilitate information and gradient propagation.

A residual connection creates a shortcut around one or more neural-network layers, combining earlier node information with transformed neighborhood information. In graph neural networks, it preserves node features, improves gradient flow, and helps prevent over-smoothing as message passing becomes deeper. section 11.6 section 11.6.4

In a GNN update, a residual connection can:

Preserve access to original node features while incorporating aggregated neighbor features. section 11.6.4
Provide additional backpropagation paths that support deeper architectures. section 11.6
Reduce feature homogenization caused by repeated message passing. section 11.6

GraphSAGE-style update

Concatenate each node’s current features with its aggregated neighbor features, transform the combined vector with a linear layer, and apply ReLU. This preserves both information sources for the next representation.

How Knowledge Graphs and LLMs in Action covers it

Cover of Knowledge Graphs and LLMs in Action

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Also explained in

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Inside Deep Learning: Math, Algorithms, ModelsA bypass path in a residual network that routes information around one or more layers so signals or shapes can be preserved.
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Sutskever's ListA residual connection is a shortcut path that carries a block's input directly to an addition with the block's transformed output.
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Build a Text-to-Image Generator (from Scratch)A direct link that transfers representations between corresponding layers or stages of a neural network.
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Learn Generative AI with PyTorchA neural-network connection that bypasses intermediate layers and passes earlier activations to later layers.
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Deep Learning with Python, Third Edition
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Deep Learning with PyTorch, Second EditionA connection that bypasses one or more network layers to provide an alternate path for activations or gradients.
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Grokking AI Algorithms, Second EditionA skip connection is a direct connection that carries an earlier layer's representation to a later corresponding layer, bypassing intervening layers.
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Math and Architectures of Deep LearningA connection that adds a layer's input to its output so information and gradients can bypass one or more layers.
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Deep Learning with R, Third EditionA residual connection is a neural-network connection that adds or otherwise passes an earlier representation to a later layer.
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Graph Neural Networks in ActionA skip connection is a neural-network pathway that passes information around one or more intermediate layers to preserve or combine representations.
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Transformers in ActionA residual connection adds a layer's input to its transformed output to facilitate optimization of deep neural networks.

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Related concepts

U-Netuses · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.
vanishing gradient problemaddresses · The vanishing-gradient problem is a neural-network training difficulty in which gradients shrink toward zero as they propagate backward, making updates to earlier layers or distant sequence steps ineffective.
ResNetused by · ResNet is a family of deep neural-network architectures built from residual blocks with shortcut connections that facilitate information flow and optimization.
transformer blockpart of · A transformer block is a neural-network module that transforms sequence representations using attention and feed-forward computation.
over-smoothingaddresses · Over-smoothing is a graph neural network failure mode in which repeated neighborhood aggregation or message passing makes node representations increasingly similar and difficult to distinguish.

This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.