Interactions between entities are often time-dependent in real-world systems such as e-commerce, social networks, streaming platforms, finance, and healthcare, and are best modeled as temporal interaction graphs. The temporal dimension plays a crucial role in modern recommendation systems, which rely on future link predictions. Temporal Graph Neural Networks (TGNN) have demonstrated state-of-the-art performance in future link prediction tasks for temporal interaction graphs. However, these models often require substantial training data unavailable in real-world settings. A potential solution to data scarcity is model pre-training on semantically related datasets. Unfortunately, transferring the TGNN model from one dataset to another is not trivial, as it contains node-specific memory modules vital for performance, resulting in them being inherently non-transferable. To overcome this limitation, we propose a novel transfer method that effectively utilizes common attributes between source and target datasets by decoupling graph nodes and corresponding attributes via bipartite encoding. This decoupling facilitates the transfer of memories and other inductive biases from source datasets to a target dataset. We evaluate the proposed transfer technique on real-world datasets and establish that it improves the performance of TGNN on the target dataset by 56% compared to the no-transfer methods and 36% over the state-of-the-art baselines in data-scarce settings.

Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model parameters). However, private verification approaches do not allow model users to verify the model at run-time. Instead, they must trust the service provider, who may tamper with the verification results. In contrast, a public verification approach that considers the possibility of dishonest service providers can benefit a wider range of users. In this paper, we propose PublicCheck, a practical public integrity verification solution for services of run-time deep models. PublicCheck considers dishonest service providers, and overcomes public verification challenges of being lightweight, providing anti-counterfeiting protection, and having fingerprinting samples that appear smooth. To capture and fingerprint the inherent prediction behaviors of a run-time model, PublicCheck generates smoothly transformed and augmented encysted samples that are enclosed around the model's decision boundary while ensuring that the verification queries are indistinguishable from normal queries. PublicCheck is also applicable when knowledge of the target model is limited (e.g., with no knowledge of gradients or model parameters). A thorough evaluation of PublicCheck demonstrates the strong capability for model integrity breach detection (100% detection accuracy with less than 10 black-box API queries) against various model integrity attacks and model compression attacks. PublicCheck also demonstrates the smooth appearance, feasibility, and efficiency of generating a plethora of encysted samples for fingerprinting.