Blockchain is an emerging technology that has already enabled a wide range of applications, from cryptocurrencies to digital asset management to supply chains. Due to this surge of popularity, analyzing the data stored on blockchains transpires as a new critical challenge in data science. To assist data scientists in various analytic tasks on a blockchain, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. We discuss how blockchain data can be abstracted as various types of networks, and how such associated network abstractions can be further used to reap important insights into the structure, organization, and functionality of blockchains.

Blockchain Networks: Data Structures of Bitcoin, Monero, Zcash, Ethereum, Ripple and Iota

Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous agents), ether (native cryptocurrency), tokens (digital assets), dApps (decentralized applications), and DeFi (decentralized finance). These key actors in the Ethereum interact with each other via transactions and contract calls. Given the highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in Ethereum blockchain. Recently, several research works performed graph analysis on the publicly available Ethereum blockchain data to reveal insights into its transactions and for important downstream tasks, e.g., cryptocurrency price prediction, address clustering, phishing scams and counterfeit tokens detection. In this work, we conduct an in-depth survey of the existing literature. We categorize them based on publication years, venues, core ranking, and authors' affiliations, data usage and graphs construction, graph mining and machine learning techniques employed, and the new insights derived by them. We conclude by discussing our recommendations on the future work. Our article will be useful to data scientists, researchers, financial analysts, and blockchain enthusiasts.

Graph Analysis of the Ethereum Blockchain Data: A Survey of Datasets, Methods, and Future Work

The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study networks with a single type of connection between vertices, while in many applications interactions between objects vary, yielding multiplex networks. We propose ANOMULY, a general, unsupervised edge anomaly detection framework for multiplex dynamic networks. In each relation type, ANOMULY sees node embeddings at different GNN layers as hierarchical node states and employs a GRU cell to capture temporal properties of the network and update node embeddings over time. We then add an attention mechanism that incorporates information across different types of relations. Our case study on brain networks shows how this approach could be employed as a new tool to understand abnormal brain activity that might reveal a brain disease or disorder. Extensive experiments on nine real-world datasets demonstrate that ANOMULY achieves state-of-the-art performance.

Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance when scaling to large virtual compound datasets. The performance of these methods has shown only incremental improvements in the past few years. To address this problem, we developed a novel method using multiparameter persistence (MP) homology that produces topological fingerprints of the compounds as multidimensional vectors. Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. We show that the margin loss fine-tuning of pretrained Triplet networks attains highly competitive results in differentiating between compounds in the embedding space and ranking their likelihood of becoming effective drug candidates. We further establish theoretical guarantees for the stability properties of our proposed MP signatures, and demonstrate that our models, enhanced by the MP signatures, outperform state-of-the-art methods on benchmark datasets by a wide and highly statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain for DUD-E Diverse dataset).

ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery

Tlife-GDN: Detecting and Forecasting Spatio-Temporal Anomalies via Persistent Homology and Geometric Deep Learning

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.

I. Segovia Dominguez

Papers

Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks.