AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 3 of 15 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A load-aware GPU-based dynamic graph pattern matching scheme is proposed to make full use of GPU computing resources and a task overhead prediction model is proposed to guide task allocation to alleviate the load imbalance between multiple GPU devices.
Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not meet the interpretability and deployment requirements of financial risk control. We propose GraphFAS (Graph Feature Automated Selection), a distributed feature selection procedure based on Boruta that bridges this gap through: (1) a non-parametric graph feature generation module that constructs explicit, interpretable structural features via multi-hop subgraph extraction and multi-scale aggregation without learned parameters; and (2) an automated distributed feature selection algorithm extending Boruta with median-based aggregation across partitions to robustly identify informative features at scale with minimal domain expertise. Compared with end-to-end GNN pipelines, GraphFAS decouples feature aggregation from model training, enabling direct integration with tabular models and direct compatibility with TreeSHAPbased explanations. Deployed in Alipay, GraphFAS delivers orderof-magnitude improvements in engineering efficiency while showing strong performance against expert-driven and graph-learning baselines on large-scale graphs.
BCE is presented, a GPU-co-designed, block-centric engine that makes range-top-k efficient by exposing a reusable intermediate representation of the data, and achieves sub-millisecond query latency and up to 308 × higher throughput than state-of-the-art GPU baselines, while performing billion-scale dynamic updates in milliseconds.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.