# Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

arXiv: https://arxiv.org/abs/1904.04849 · HTML: https://mlsanity.com/paper/1904.04849/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 20 of 20)

- [Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective](https://mlsanity.com/paper/2112.14531/) (2021)
- [A Graph Neural Network with Negative Message Passing for Graph Coloring](https://mlsanity.com/paper/2301.11164/) (2023)
- [Learn Layer-wise Connections in Graph Neural Networks](https://mlsanity.com/paper/2112.13585/) (2021)
- [Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes](https://mlsanity.com/paper/2408.11351/) (2024)
- [NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique](https://mlsanity.com/paper/2102.08588/) (2021)
- [MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2412.10467/) (2024)
- [Dual GNNs: Graph Neural Network Learning with Limited Supervision](https://mlsanity.com/paper/2106.15755/) (2021)
- [AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020](https://mlsanity.com/paper/2111.12952/) (2021)
- [Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications](https://mlsanity.com/paper/2207.09955/) (2022)
- [Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis](https://mlsanity.com/paper/2408.15305/) (2024)
- [HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning](https://mlsanity.com/paper/2410.21861/) (2024)
- [Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation](https://mlsanity.com/paper/2408.07624/) (2024)
- [Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning](https://mlsanity.com/paper/2408.12409/) (2024)
- [Multi-Knowledge Fusion Network for Time Series Representation Learning](https://mlsanity.com/paper/2408.12423/) (2024)
- [Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption](https://mlsanity.com/paper/2408.13248/) (2024)
- [Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models](https://mlsanity.com/paper/2408.13621/) (2024)
- [Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models](https://mlsanity.com/paper/2408.13661/) (2024)
- [EMCNet : Graph-Nets for Electron Micrographs Classification](https://mlsanity.com/paper/2409.03767/) (2024)
- [Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis](https://mlsanity.com/paper/2409.07463/) (2024)
- [Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis](https://mlsanity.com/paper/2409.12244/) (2024)

## Abstract (arXiv)

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring embeddings of potentially differing locality. In order to avoid overfitting, we propose to control the channel-wise connections between input and output by making use of grouped linear projections. In a number of transductive node-classification experiments, we demonstrate the effectiveness of our approach.