# Modeling Smart Contracts Activities: A Tensor Based Approach

## TL;DR

This paper proposes a novel tensor decomposition method for predicting interactions among smart contracts on blockchain platforms, enhancing security and efficiency in smart contract execution.

## Contribution

It introduces a tensor-based approach using CANDECOMP/PARAFAC for temporal link prediction in smart contracts, integrating stochastic processes for series forecasting.

## Key findings

- Effective prediction of smart contract interactions
- Improved security and transaction management
- Novel application of tensor decomposition in blockchain analytics

## Abstract

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.