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

2026-02-27

AI Summary

This work proposes COVE, a high-dimensional and interpretable node embedding method designed to overcome the limitations of traditional low-dimensional approaches in capturing complex structural relationships in graphs. COVE integrates co-occurrence statistics from random walks with nonlinear dimensionality reduction via UMAP and incorporates diffusion-inspired modeling to characterize node similarity. Coupled with HDBSCAN clustering, the method demonstrates strong performance on link prediction and community detection tasks. It achieves clustering quality comparable to the Louvain algorithm and slightly outperforms leading baseline models across multiple benchmark datasets, thereby validating the advantage of high-dimensional embeddings in preserving graph structural information.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• node embedding
• non-linear dimension reduction
• random walk co-occurrence
• high dimensional embedding
• community detection

Innovation

• non-linear dimension reduction
• random walk co-occurrence
• high-dimensional node embedding
• explainable embedding
• UMAP