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Scalable Deep Metric Learning on Attributed Graphs

arXiv 2411.13014 ↗First posted 20 Nov 2024cs.LGcited by 0

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Abstract (arXiv)

We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques to 1) work with attributed graphs, 2) enabling a mini-batch based approach, and 3) achieving scalability. Based on a multi-class tuplet loss function, we present two algorithms -- DMT for semi-supervised learning and DMAT-i for the unsupervised case. Analyzing our methods, we provide a generalization bound for the downstream node classification task and for the first time relate tuplet loss to contrastive learning. Through extensive experiments, we show high scalability of representation construction, and in applying the method for three downstream tasks (node clustering, node classification, and link prediction) better consistency over any single existing method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 57 references that are in the map. First posted on arXiv 20 Nov 2024.

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References · 37

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25 more
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