Summary of Node Identifiers: Compact, Discrete Representations For Efficient Graph Learning, by Yuankai Luo et al.
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
by Yuankai Luo, Hongkang Li, Qijiong Liu, Lei Shi, Xiao-Ming Wu
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel end-to-end framework for generating compact, discrete, and interpretable node representations, called node identifiers (node IDs), to address inference challenges on large-scale graphs. The framework employs vector quantization to compress continuous node embeddings from multiple layers of a Graph Neural Network (GNN) into discrete codes, applicable under both self-supervised and supervised learning paradigms. Node IDs capture high-level abstractions of graph data and offer interpretability that traditional GNN embeddings lack. Extensive experiments on 34 datasets demonstrate that the generated node IDs significantly enhance speed and memory efficiency while achieving competitive performance compared to current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to understand big graphs by making it easier to work with them. It uses a special kind of neural network called Graph Neural Network (GNN) to shrink down the information about each part of the graph into a few numbers, called node identifiers (node IDs). These node IDs are easy to understand and can be used for different tasks like classifying nodes or predicting connections between them. The paper tested this method on 34 different datasets and showed that it can work faster and use less memory than other methods. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Inference » Neural network » Quantization » Self supervised » Supervised
