Node Embeddings via Neighbor Embeddings
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 48 of its 78 references that are in the map. First posted on arXiv 31 Mar 2025.
Builds on
48 of 78 references.
References · 48
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2014DeepWalk: Online Learning of Social Representationscited by 1,543
- 2020Graph Contrastive Learning with Augmentationscited by 1,203
- 2018Deep Graph Infomaxcited by 1,101
- 2018Link Prediction Based on Graph Neural Networkscited by 829
36 more
- 2020Contrastive Multi-View Representation Learning on Graphscited by 651
- 2020Deep Graph Contrastive Representation Learningcited by 535
- 2019A Theoretical Analysis of Contrastive Unsupervised Representation Learningcited by 530
- 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
- 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 401
- 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
- 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
- 2016Exploring the Limits of Language Modelingcited by 172
- 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
- 2018Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiencycited by 103
- 2021Augmentation-Free Self-Supervised Learning on Graphscited by 94
- 2017Efficient Algorithms for t-distributed Stochastic Neighborhood Embeddingcited by 75
- 2017Clustering with t-SNE, provablycited by 51
- 2020Attraction-Repulsion Spectrum in Neighbor Embeddingscited by 41
- 2020word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Datacited by 38
- 2021On UMAP's true loss functioncited by 32
- 2022Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embeddingcited by 28
- 2022From t-SNE to UMAP with contrastive learningcited by 28
- 2023Graph Neural Networks Use Graphs When They Shouldn'tcited by 25
- 2021Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practicescited by 24
- 2023A Message Passing Perspective on Learning Dynamics of Contrastive Learningcited by 22
- 2022Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representationscited by 21
- 2019A Comparative Study for Unsupervised Network Representation Learningcited by 16
- 2022Unsupervised visualization of image datasets using contrastive learningcited by 16
- 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasetscited by 16
- 2022Localized Contrastive Learning on Graphscited by 10
- 2020NCVis: Noise Contrastive Approach for Scalable Visualizationcited by 9
- 2021Should Graph Neural Networks Use Features, Edges, Or Both?cited by 7
- 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learningcited by 6
- 2019GraphTSNE: A Visualization Technique for Graph-Structured Datacited by 5
- 2020DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reductioncited by 5
- 2023Force-Directed Graph Layouts Revisited: A New Force Based on the T-Distributioncited by 5
- 2023Balancing between the Local and Global Structures (LGS) in Graph Embeddingcited by 5
- 2021Stochastic Cluster Embeddingcited by 3
- 2019Spaceland Embedding of Sparse Stochastic Graphscited by 2
- 2021Effective and Efficient PageRank-based Positioning for Graph Visualizationcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.