mlsanityData to 7 Oct 2026

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences

arXiv 2202.03341 ↗First posted 7 Feb 2022cs.LGcited by 4

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Abstract (arXiv)

Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields. However, this recursive design inherently leads to excessive computation and memory requirements, making it not applicable to massive real-world graphs. In this work, we propose the Neighbor2Seq to transform the hierarchical neighborhood of each node into a sequence. This novel transformation enables the subsequent mini-batch training for general deep learning operations, such as convolution and attention, that are designed for grid-like data and are shown to be powerful in various domains. Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency and advantages of deep learning operations on grid-like data by precomputing the Neighbor2Seq transformations. We evaluate our method on a massive graph, with more than 111 million nodes and 1.6 billion edges, as well as several medium-scale graphs. Results show that our proposed method is scalable to massive graphs and achieves superior performance across massive and medium-scale graphs. Our code is available at https://github.com/divelab/Neighbor2Seq.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 40 of its 49 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

40 of 49 references.

References · 40

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28 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.

4 papers.

  1. 2022A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinkingcited by 56
  2. 2022GraphFM: Improving Large-Scale GNN Training via Feature Momentumcited by 31
  3. 2024PipeNet: Question Answering with Semantic Pruning over Knowledge Graphscited by 1
  4. 2025Evolutionary Architecture Search through Grammar-Based Sequence Alignmentcited by 0