Modeling Smart Contracts Activities: A Tensor Based Approach
For blockchain researchers and developers, this work addresses predictive analytics of smart contract interactions, but the lack of empirical validation makes it an incremental proposal.
The paper proposes a tensor decomposition-based method (CANDECOMP/PARAFAC) for temporal link prediction of smart contract interactions on Ethereum, aiming to forecast payment transactions. No concrete results or performance numbers are provided.
Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum platform, an open-source blockchain-based platform, smart contracts implement a distributed virtual machine on the distributed ledger. To avoid denial of service attacks and monetize the services, payment transactions are executed whenever code is being executed between contracts. It is thus natural to investigate if predictive analysis is capable to forecast these interactions. We have addressed this issue and propose an innovative application of the tensor decomposition CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. We introduce a new approach leveraging stochastic processes for series predictions based on the tensor decomposition that can be used for smart contracts predictive analytics.