While large language models have revolutionized Text-to-SQL tasks, translating natural language into Graph Query Languages (Text2GQL) remains underexplored due to the topological heterogeneity and syntactic diversity of graph query languages (e.g., Cypher, Gremlin, SPARQL).Existing approaches often struggle with structural hallucinations and lack adaptability in cold-start scenarios.In this paper, we present a unified, trainingfree Text2GQL framework.First, Structural Twig Linking elevates schema grounding to the identification of semantic substructures ("twigs"), providing robust topological priors.Second, addressing data scarcity, Evolutionary In-Context Learning operates in a Tabula Rasa setting to implicitly construct a selfgrowing repository of verified examples driven by syntactic utility.Finally, our Adversarial Execution-Guided Correction agent enforces fidelity through synergistic static critique and dynamic verification.Experiments demonstrate significant improvements over baselines in both accuracy and executability across diverse GQLs.The code is available at https: //github.com/nf202/Text2Graph.

Recently, several social network analysis algorithms have been optimized by leveraging the community structure commonly found in graphs. Since community structure is fun-damental to these algorithms, storing graphs based on their community structure can significantly enhance the performance of graph algorithms that rely on it for optimization. However, existing graph storage systems do not natively store graphs according to the community structure, which limits their per-formance in retrieving communities. To fill this gap, we pro-pose LSM-Community, a graph storage system inspired by the LSM - Tree design that stores graphs on disk based on their community structure. To dynamically maintain the community structure during graph updates, we present the community-centric dynamic community detection algorithm$(C^{3}D)$. Experimental results demonstrate that LSM-Community outperforms other storage systems in classical community discovery tasks (e.g., performing CD on UK-2007 dataset with LSM-Community is$86.12\times$faster than Neo4j) while maintaining high performance on classical graph analytic algorithms. This indicates that LSM-Community efficiently supports community discovery and query processing while preserving the performance of classical analytic algorithms,

With the rapid growth of data and the growing demand from users on the system performance, data availability has become the most important issue in large-scale storage systems. Due to the ability to provide space-optimal data redundancy to protect against node failures, erasure codes have seen widely deployment. To ensure data availability, it is crucial to recover node failures quickly. In this paper, we propose PHR, a pipelined heterogeneous recovery optimization for failure recovery in RAID-6 coded storage systems. Our PHR takes into account both I/O parallelism and node heterogeneity in practical storage systems, and returns an efficient recovery solution timely. We parallelize our PHR algorithm in a pipelined manner, so as to further improve failure recovery performance. With the quantitative simulation studies and extensive test bed experiments, we show our PHR significantly reduces recovery time and also user response time.

This paper presents a novel Q-learning based auction (QL-BA) algorithm for dynamic spectrum access in a one primary user multiple secondary users (OPMS) scenario. In the auction market, the secondary user provides a bidding price dynamically and intelligently using a Q-learning based bidding strategy to compete for current access opportunity; meanwhile primary user decides to whom to release the unused spectrum according to the maximal bidding principle. To obtain the limited and time-varying spectrum opportunities, each bidder presents a preference utility through Q-learning, considering the current packet transmission and future expectation. Simulation results show that the proposed QL-BA can significantly improve secondary users' bidding strategies and, hence, the performance in terms of packet loss, bidding efficiency and transmission rate is improved progressively.