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CAT is proposed, the first Context-Aware GNN-based Trust prediction model that supports trust dynamicity and accurately represents real-world heterogeneity, and introduces a new notion of meta-paths to extract contextual features.
LLM4Trust is proposed, the first benchmark framework that systematically explores the capabilities of Large Language Models for trust evaluation and proposes a defense mechanism and batch inference to improve the robustness and efficiency of LLM-based trust evaluation.
The global rollout of 5G networks has spurred the rapid deployments of edge servers for hosting latency-sensitive web applications, which improves quality of experience (QoE). However, current efforts fall short in the substantial energy costs associated with the 24/7 operation of edge servers and overlook user privacy...
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