@misc{723,
  keywords = {Blockchain Analytics, Network Science, Decentralized Finance (DeFi), Network Motifs, Graph Representation Learning, Cryptocurrency Network Analysis},
  author = {Natkamon Tovanich},
  title = {Analysis of User Behaviors in Blockchain : A Network Science Perspective},
  abstract = {<p>Public blockchains offer transparent transaction records, yet identifying actors and interpreting their activities remains challenging. This presentation brings together my research at three levels: (i) micro-level, using network motifs and graphlets to classify DeFi transaction methods; (ii) meso-level, combining money flow analysis and graph representation learning to identify pseudonymous entities; and (iii) macro-level, using multilayer token networks to analyze cross-token trading strategies. Applications include inferring account roles from motifs, identifying entities from money flow representations, and tracking Alameda Research’s trading behavior before bankruptcy. Together, these approaches translate blockchain data into interpretable insights into actors, operations, and financial strategies.</p>},
  year = {2026},
  journal = {2nd Transparency, Regulation, and Analytics in Crypto-Asset Ecosystems workshop (TRACE 2026)},
  month = {10/2026},
  address = {Florence},
  doi = {10.34726/12879},
}