Structure-aware Semantic Node Identifiers for Learning on Graphs
CoRR(2024)
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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