Title

Keynote: Dissecting Blockchain Analytics: What We Can Learn from Topology and Geometry of Blockchain Transaction Graphs

Presenter Information/ Coauthors Information

Yulia R. Gel, University of Texas at Dallas

Presentation Type

Keynote

Abstract

Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties, crypto price dynamics as well as illegal and illicit activities such as emerging ransomware.

In this talk we discuss horizons and limitations of what new can be learned from topology and geometry of cryptocurrency transaction graphs whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis, functional data depth, network motifs, and geometric deep learning, we show that even some subtler patterns in blockchain transaction graphs can provide critical insights for money laundering tracking, price analytics, and market sentiment assessment.

Start Date

2-7-2022 7:30 PM

End Date

2-7-2022 8:30 PM

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Feb 7th, 7:30 PM Feb 7th, 8:30 PM

Keynote: Dissecting Blockchain Analytics: What We Can Learn from Topology and Geometry of Blockchain Transaction Graphs

Oscar Larson Performing Arts Center

Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties, crypto price dynamics as well as illegal and illicit activities such as emerging ransomware.

In this talk we discuss horizons and limitations of what new can be learned from topology and geometry of cryptocurrency transaction graphs whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis, functional data depth, network motifs, and geometric deep learning, we show that even some subtler patterns in blockchain transaction graphs can provide critical insights for money laundering tracking, price analytics, and market sentiment assessment.