ledger / on‑chain data analysis

The rapidly growing demand for blockchain data analytics lacks a systematic, holistic characterization in existing surveys. To address this gap, we conduct a rigorous scoping review of 466 studies following the PRISMA-ScR guidelines, establishing— for the first time—a structured, end-to-end thematic framework covering the entire blockchain data analytics pipeline. Our analysis identifies six core themes, with illicit activity detection (38%) and financial analytics (29%) dominating the literature, while cross-domain applications such as business intelligence remain markedly underexplored. We uncover critical methodological gaps, including fragmented analytical approaches and insufficient contextual adaptation to real-world blockchain use cases. Based on these findings, we propose three key future directions: enhancing model interpretability, enabling interoperable multi-chain analytics, and closing the business-value loop through actionable insights. This work provides both a theoretical roadmap and practical guidance for unlocking the full analytical potential of blockchain data.

Blockchain analytics faces critical challenges including insufficient integration of blockchain technology with data analytics, low on-chain data accessibility, poor scalability, compromised analytical accuracy, and weak cross-system interoperability. Method: We systematically survey both academic research and industrial practice to construct, for the first time, a comprehensive blockchain analytics landscape encompassing dual academic–industrial perspectives. We propose a four-dimensional taxonomy—“tool type × data dimension × analytical layer × ecosystem role”—to characterize and differentiate tools such as blockchain explorers, on-chain service providers, research platforms, and market data vendors, clarifying their technical positioning and interoperability bottlenecks. Through systematic literature review and multi-source tool comparison, we identify key structural gaps. Contribution: Our work provides theoretical foundations and actionable pathways for standardizing data interfaces, building trustworthy analytical frameworks, and strengthening academia–industry collaboration in blockchain analytics.

This study addresses a critical gap in blockchain research, which has predominantly focused on on-chain transactions while neglecting the comprehensive lifecycle management of cryptographic assets. For the first time, the paper introduces the ISO 15489-1:2016 records management standard into the blockchain domain, leveraging records lifecycle theory to propose a seven-stage data lifecycle framework spanning from creation to disposition. The applicability of this model is demonstrated through case studies involving Bitcoin, fungible tokens, and non-fungible tokens. By elucidating the inherent characteristics of blockchain as a records management system, the framework clarifies the boundaries between on-chain and off-chain data and examines how privacy-enhancing technologies affect lifecycle visibility. This structured perspective offers valuable insights for the governance of crypto-assets, regulatory compliance, and forensic investigations.

This study addresses the limitations of existing blockchain behavior analysis methods, which are often confined to specific applications, lack scalability, and offer limited interpretability—hindering long-term, large-scale forensic investigations. To overcome these challenges, this work proposes a scalable, application-agnostic analytical framework that constructs behavioral statements by integrating contract, token, and market context. It models users’ long-term behavioral patterns through sentence-level and sequence-level embeddings and generates interpretable community profiles based on behavioral motifs, routines, and temporal dynamics. Notably, this approach is the first to combine persistent behavioral pattern discovery with explainable analysis, moving beyond conventional snapshot-based methods. Evaluated on 30 million Ethereum transactions, the framework successfully identifies diverse behaviors—including DEX trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pulls—with most patterns demonstrating stability across time windows.

Anti-money laundering (AML) research leveraging network analysis (NA) is fragmented, lacking systematic surveys and comparable empirical evaluations. Method: We conduct the first large-scale systematic literature review covering 97 papers and an accompanying empirical study. We propose the first structured taxonomy for NA-AML, develop a reproducible and extensible standardized benchmark, and uniformly evaluate three representative method classes—handcrafted features, random-walk-based embeddings (DeepWalk, Node2Vec), and graph neural networks (GNNs)—on public AML datasets. Contribution/Results: Our findings show that NA significantly improves money laundering detection performance; however, GNNs exhibit limited robustness under class imbalance and complex graph topologies. Moreover, existing open-source AML datasets suffer from representativeness bias. This work establishes a theoretical framework, an evaluation paradigm, and open-source tools for NA-AML, thereby advancing method standardization and reproducible research.

Detecting covertly stored sensitive or illicit content—such as personal privacy data, pornographic images, and divisive or discriminatory speech—on the decentralized, immutable Ethereum blockchain poses significant challenges. Method: We propose the first multimodal collaborative detection framework tailored for Ethereum. It features a novel on-chain fragmented-data reconstruction algorithm to structurally restore text, images, and files; integrates FastText-based sentiment analysis, NSFWJS-based image classification, and on-chain semantic parsing. Contribution/Results: We empirically uncover and validate a previously undocumented phenomenon: targeted embedding of sensitive information concerning Chinese public officials. Our framework successfully reconstructed 175 files, 296 images, and 91,206 text segments. Evaluation shows 90% accuracy in sentiment classification and 100% precision (7/7) in identifying non-safe-for-work (NSFW) images. This work establishes a foundational methodology for forensic analysis and content moderation on permissionless blockchains.

This work addresses the non-determinism in real-time stream processing—caused by factors such as scheduling, window triggering, out-of-order data, and network jitter—which hinders auditability and reproducibility. The authors propose a lightweight blockchain-based provenance architecture that, for the first time, integrates blockchain as a cryptographic anchor into Kafka Streams. Instead of storing raw payloads on-chain, the system records only Merkle roots of windowed data along with offset metadata, preserving data privacy and system performance while enabling verifiable integrity, sequence consistency, and analytical correctness of processing results. Experiments using a real-world weather dataset from Berlin demonstrate that the approach supports fully deterministic replay, incurs linearly scalable verification overhead, and achieves high-throughput blockchain integration, effectively meeting the demands of real-time stream processing scenarios.

Traditional blockchain forensic methods rely on static reasoning and struggle to meet the demands of dynamic investigations such as cross-chain tracing. This work proposes LOCARD, the first agent-driven blockchain forensic framework, which formulates forensics as a sequential decision-making process. LOCARD employs a tripartite cognitive architecture to decouple policy planning, operational execution, and verification evaluation, and introduces a structured belief state to embed forensic rigor constraints. Integrating large language model agents, cross-chain transaction modeling, and a Sybil cluster decomposition algorithm, LOCARD demonstrates high-fidelity tracing of money-laundering subflows from the Bybit hack, validated on Thor25—a novel benchmark comprising 151,000 real-world records.

This study addresses the technical auditing challenges posed by blockchain assets and custody mechanisms, which disrupt traditional financial audit paradigms—particularly concerning asset existence, ownership verification, valuation, and internal control validation. For the first time, autoethnography is introduced into blockchain auditing, drawing on the author’s multifaceted experience as a blockchain engineer, smart contract auditor, and CTO of a crypto-asset firm to uncover nuanced technical details inaccessible to external observers. Focusing on representative scenarios such as airdropped tokens, multi-signature smart contracts, and real-time on-chain reporting, the work constructs an audit analysis framework tailored to emerging on-chain practices and proposes reusable, experimental audit pathways that effectively tackle challenges currently deemed technically insurmountable.

This study addresses the current lack of systematic empirical data on identity, reputation, and interaction behaviors of blockchain-registered AI agents. We present the first structured dataset of Ethereum-based AI agents conforming to the ERC-8004 standard, constructed by collecting on-chain events—such as identity registration, minting, and transfers—from the Ethereum mainnet via Web3 RPC, and integrating them with off-chain metadata. The resulting dataset comprises event-level records and aggregated summaries for 10,000 agents, enabling reproducible research into critical aspects of decentralized AI ecosystems, including identity formation, reputation mechanisms, and service exposure. This resource provides foundational support for advancing research in agent economies grounded in decentralized infrastructure.