mlsanityData to 14 Oct 2026

RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks

arXiv 2503.11910 ↗First posted 14 Mar 2025cs.LG cs.AI math.ATcited by 4

Not on the map. Why?

Abstract (arXiv)

Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at arbitrary scales, of two weighted graphs with one-to-one correspondence between vertices. Using minimal spanning trees in auxiliary graphs, RTD-Lite captures topological discrepancies with O(n2) time and memory complexity. This efficiency enables its application in tasks like dimensionality reduction and neural network training. Experiments on synthetic and real-world datasets demonstrate that RTD-Lite effectively identifies topological differences while significantly reducing computation time compared to existing methods. Moreover, integrating RTD-Lite into neural network training as a loss function component enhances the preservation of topological structures in learned representations. Our code is publicly available at https://github.com/ArGintum/RTD-Lite

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

22 of 51 references.

References · 22

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10 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scalingcited by 8
  2. 2026What Converges in the Platonic Representation Hypothesis? Structure over Geometrycited by 1
  3. 2025Edge-wise Topological Divergence Gaps: Guiding Search in Combinatorial Optimizationcited by 0
  4. 2026Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysiscited by 0