LearnIng and Mining for BlOckchains - LIMBO
Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD 2023
18 September 2023
Turin, Italy

WORKSHOP WEBSITE: https://sites.google.com/view/limboecmlpkdd2023

CONTEXT
Blockchain-based technologies and the Web3 paradigm are gaining significant attention and importance in today's world. The main reason is their decentralized, secure, and transparent nature. Blockchain technologies have the potential to enable new forms of innovation and collaboration, which is why they are increasingly being used in various domains. Some of these applications include decentralized finance (DeFi), non-fungible tokens (NFT), decentralized identity management, and decentralized autonomous organization (DAO). Blockchain-based technologies are also playing a crucial role in the development of the metaverse.

In the landscape of blockchain-based solutions, machine learning and data mining are becoming increasingly important. This is because blockchain technologies face various challenges such as preserving and increasing the security of blockchain networks, optimizing the performance of smart contracts, and managing large-scale graphs. Machine learning and data mining can help in coping with these challenges. Moreover, blockchain-based technologies are appealing to the data mining and machine learning community for another key aspect: data. Since blockchain technologies support transparency, immutability, and validation by design, they represent invaluable data sources for different application domains such as financial time series forecasting, mining and learning on large-scale graphs, and malicious and fraudulent behavior in transaction and relational data. Researchers can access large collections of temporal and heterogeneous data, which capture different aspects of the interactions among the elements defining blockchain-based applications and platforms. Due to the richness, quality, and high temporal resolution of blockchain data, different research areas of machine learning and data mining can benefit from data coming from blockchain networks.

SCOPE AND TOPICS
The LearnIng and Mining for BlOckchains - LIMBO workshop at ECML PKDD 2023 aims at bringing together researchers and practitioners working on different areas in machine learning and data mining as well as experts and researchers in blockchain-based technologies and solutions, to present and discuss recent developments, to identify open issues, to form a community and foster future initiatives. We welcome submissions that present contributions related but not limited to the following topics:
- Decentralized federated learning on blockchains
- Anomaly detection in blockchains
- Graph neural networks for blockchain data
- Continual learning for blockchain data
- Machine learning and data mining for de-anonymization in blockchain
- Address clustering in blockchain
- Machine learning and data mining for identifying blockchain manipulation
- Machine learning for fraud detection in blockchains
- Machine learning and data mining for wash trading and money laundering
- Graph mining for networked blockchain data
- Graph mining for temporal and/or networked blockchain data
- Forecasting for temporal blockchain data
- Machine learning for IoT platforms on blockchain
- Analytics platforms for blockchain data
- Machine learning for smart contract optimization
- Machine learning and data mining for smart contract analysis
- Identification of security threats by machine learning and data mining
- Identification of malicious behaviors in blockchains

IMPORTANT DATES
- Paper submission deadline: 12 June 2023
- Paper author notification: 12 July 2023
- Camera-ready deadline: July 28, 2023

SUBMISSION WEBSITE: https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Submission/Index

SUBMISSION INSTRUCTION
-- Full/short Paper Submissions --
We welcome submissions of papers of two types: full papers up to 12 pages and short papers up to 8 pages (excluding references) in LNCS format. All submitted papers will undergo a single-blind peer review process, and be selected based on originality, quality, soundness, and relevance. Submissions will be evaluated by at least two reviewers. Contributions should be in PDF format and submitted via CMT. Accepted long and short papers will be in the joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science.

-- Extended Abstract Submissions --
We also welcome submissions of extended abstracts with up to 4 pages (excluding references) in LNCS format. Extended abstracts can cover material already published by the author, or can be an abstract of late-breaking results. Contributions should be in PDF format and submitted via CMT. Extended abstracts will not be published in the proceedings, but can be accepted as presentations to the workshop.

PC-CHAIRS
Sabrina Gaito, University of Milan, Milan, Italy - sabrina.gaito@unimi.it
Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France - roberto.interdonato@cirad.fr
Andrea Tagarelli, University of Calabria, Cosenza, Italy - andrea.tagarelli@unical.it
Matteo Zignani, University of Milan, Milan, Italy - matteo.zignani@unimi.it