Structure-aware Semantic Node Identifiers for Learning on Graphs
- URL: http://arxiv.org/abs/2405.16435v1
- Date: Sun, 26 May 2024 05:22:38 GMT
- Title: Structure-aware Semantic Node Identifiers for Learning on Graphs
- Authors: Yuankai Luo, Qijiong Liu, Lei Shi, Xiao-Ming Wu,
- Abstract summary: We present a novel graph tokenization framework that generates structure-aware, semantic node identifiers (IDs) in the form of a short sequence of discrete codes.
We employ vector quantization to compress continuous node embeddings from multiple layers of a graph neural network (GNN), into compact, meaningful codes.
The resulting node IDs capture a high-level abstraction of graph data, enhancing the efficiency and interpretability of GNNs.
- Score: 8.216962546682566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a novel graph tokenization framework that generates structure-aware, semantic node identifiers (IDs) in the form of a short sequence of discrete codes, serving as symbolic representations of nodes. We employs vector quantization to compress continuous node embeddings from multiple layers of a graph neural network (GNN), into compact, meaningful codes, under both self-supervised and supervised learning paradigms. The resulting node IDs capture a high-level abstraction of graph data, enhancing the efficiency and interpretability of GNNs. Through extensive experiments on 34 datasets, including node classification, graph classification, link prediction, and attributed graph clustering tasks, we demonstrate that our generated node IDs not only improve computational efficiency but also achieve competitive performance compared to current state-of-the-art methods.
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