Paper: arXiv 2603.18021

Authors: Illia Donhauzer, Pierluigi Cesana, Tomoyuki Shirai, Yuichi Ikeda

Abstract

The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an interesting application of Topological Data Analysis (TDA), specifically Betti increments and network motifs, to predict anomalous XRP price surges. It combines decent mathematical formalization of Vietoris-Rips filtrations with practical empirical feature engineering on transaction network graphs.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict XRP Price Surges via Topological Features] --> B[Input Data: XRP On-Chain Transactions & Price History]
    B --> C[Construct Transaction Graphs & Apply TDA]
    C --> D[Extract Topological Properties & Compute Features]
    D --> E[Train ML Models for Extreme Value Prediction]
    E --> F[Key Outcome: Topological Features Significantly Improve Anomaly Forecasting]

Cited References (17)

  1. Stochastic Modeling of UTXOs in Bitcoin Graphs (2024) — link
  2. Dynamic relationship between the XRP price and correlation tensor spectra of transaction networks (2024) — link
  3. Persistent Homology and Its Application to Chainlets in the Bitcoin Graph (2023) — link
  4. Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis (2022) — link
  5. Projecting XRP price burst by correlation tensor spectra of transaction networks (2022) — link
  6. Cryptoasset networks: Flows and regular players in Bitcoin and XRP (2022) — link
  7. ChainNet: Learning on Blockchain Graphs with Topological Features (2019) — link
  8. Forecasting Bitcoin Price with Graph Chainlets (2018) — link
and 9 more
  1. A Unified Approach to Interpreting Model Predictions (2017) — link
  2. Linear-Size Approximations to the Vietoris–Rips Filtration (2012) — link
  3. Topology and data (2009) — link
  4. Computing Persistent Homology (2004) — link
  5. Topological Persistence and Simplification (2000) — link
  6. SoK: Advances in Anomaly Detection Techniques for Cryptoasset Transactions (2025) — link
  7. Link to Networkx
  8. Link to XRP court decision
  9. Link to Puell multiple data

0 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.