mlsanityData to 14 Oct 2026

Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding

arXiv 2602.24069 ↗First posted 27 Feb 2026cs.LG cs.SIcited by 0

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Abstract (arXiv)

Leveraging non-linear dimension reduction techniques, we remove the low dimension constraint from node embedding and propose COVE, an explainable high dimensional embedding that, when reduced to low dimension with UMAP, slightly increases performance on clustering and link prediction tasks. The embedding is inspired by neural embedding methods that use co-occurrence on a random walk as an indication of similarity, and is closely related to a diffusion process. Extending on recent community detection benchmarks, we find that a COVE UMAP HDBSCAN pipeline performs similarly to the popular Louvain algorithm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 55 references that are in the map. First posted on arXiv 27 Feb 2026.

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21 of 55 references.

References · 21

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  5. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  6. 2015LINE: Large-scale Information Network Embeddingcited by 582
  7. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  8. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 198
  9. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 173
  10. 2020Understanding graph embedding methods and their applicationscited by 51
  11. 2020Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structurecited by 19
  12. 2020Community detection in networks using graph embeddingscited by 11
9 more
  1. 2019Mercator: uncovering faithful hyperbolic embeddings of complex networkscited by 10
  2. 2024Dimension Reduction with Locally Adjusted Graphscited by 9
  3. 2019Link prediction with hyperbolic geometrycited by 7
  4. 2023Network community detection via neural embeddingscited by 7
  5. 2013Model of complex networks based on citation dynamicscited by 6
  6. 2021A Multi-purposed Unsupervised Framework for Comparing Embeddings of Undirected and Directed Graphscited by 6
  7. 2019Ensemble Clustering for Graphs: Comparisons and Applicationscited by 3
  8. 2019Community Detection in the Hyperbolic Spacecited by 3
  9. 2026A Pragmatic Method for Comparing Clusterings with Overlaps and Outlierscited by 1

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