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arXiv · 2503.23822

Node Embeddings via Neighbor Embeddings

Jan Niklas Böhm·Marius Keute·Alica Guzmán·Sebastian Damrich·Andrew Draganov·Dmitry Kobak
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Abstract

Node embeddings are a paradigm in non-parametric graph representation learning, where graph nodes are embedded into a given vector space to enable downstream processing. State-of-the-art node-embedding algorithms, such as DeepWalk and node2vec, are based on random-walk notions of node similarity and on contrastive learning. In this work, we introduce the graph neighbor-embedding (graph NE) framework that directly pulls together embedding vectors of adjacent nodes without relying on any random walks. We show that graph NE strongly outperforms state-of-the-art node-embedding algorithms in terms of local structure preservation. Furthermore, we apply graph NE to the 2D node-embedding problem, obtaining graph t-SNE layouts that also outperform existing graph-layout algorithms.

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Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak. 2025-03-31. Node Embeddings via Neighbor Embeddings. https://arxiv.org/abs/2503.23822

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