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Blockchains facilitate secure resource transactions through smart contracts, yet these digital agreements are prone to vulnerabilities, particularly when interacting with external contracts, leading to substantial monetary losses. Traditional verification techniques fall short in providing comprehensive security assurances, especially against re-entrancy attacks, due to the unavailable implementations of external contracts. This paper introduces an incremental approach: gradual verification. We combine static and dynamic verification techniques to enhance security, guarantee soundness and flexibility, and optimize resource usage in smart contract interactions. By implementing a prototype for gradually verifying Algorand smart contracts via the pyTEAL language, we demonstrate the effectiveness of our approach, contributing to the safe and efficient execution of smart contracts.

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm.