mlsanityData to 7 Oct 2026

GGNNs : Generalizing GNNs using Residual Connections and Weighted Message Passing

arXiv 2311.15448 ↗First posted 26 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Many real-world phenomena can be modeled as a graph, making them extremely valuable due to their ubiquitous presence. GNNs excel at capturing those relationships and patterns within these graphs, enabling effective learning and prediction tasks. GNNs are constructed using Multi-Layer Perceptrons (MLPs) and incorporate additional layers for message passing to facilitate the flow of features among nodes. It is commonly believed that the generalizing power of GNNs is attributed to the message-passing mechanism between layers, where nodes exchange information with their neighbors, enabling them to effectively capture and propagate information across the nodes of a graph. Our technique builds on these results, modifying the message-passing mechanism further: one by weighing the messages before accumulating at each node and another by adding Residual connections. These two mechanisms show significant improvements in learning and faster convergence

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 23 references that are in the map. First posted on arXiv 26 Nov 2023.

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References · 22

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10 more
  1. 2020Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Losscited by 267
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