Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

TL;DR

This paper introduces a topological method using clique persistent homology to detect anomalies in dynamic multilayer blockchain networks, effectively capturing complex structural changes.

Contribution

It develops a novel stacked persistence diagram for multilayer networks and demonstrates its stability and superior performance in blockchain anomaly detection.

Findings

Outperforms existing anomaly detection techniques

Successfully applied to Ethereum and Ripple networks

Provides a stable topological summary under data perturbations

Abstract

Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we introduce a new topological perspective to structural anomaly detection in dynamic multilayer networks. We postulate that anomalies in the underlying blockchain transaction graph that are composed of multiple layers are likely to also be manifested in anomalous patterns of the network shape properties. As such, we invoke the machinery of clique persistent homology on graphs to systematically and efficiently track evolution of the network shape and, as a result, to detect changes in the underlying network topology and geometry. We develop a new persistence summary for multilayer networks, called stacked persistence diagram, and prove its stability under input data perturbations. We validate our new topological anomaly detection framework in application to dynamic multilayer networks from the Ethereum Blockchain and the Ripple Credit Network, and demonstrate that our stacked PD approach substantially outperforms state-of-art techniques.