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Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks.

, , , , , , , , , and . ACM Comput. Surv., 56 (5): 126:1-126:42 (May 2024)

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Automated Feature Selection: A Reinforcement Learning Perspective., , , , , and . IEEE Trans. Knowl. Data Eng., 35 (3): 2272-2284 (March 2023)NRGNN: Learning a Label Noise Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs., , and . KDD, page 227-236. ACM, (2021)Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities., , , , , and . SIGIR, page 3425-3428. ACM, (2022)Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities., , and . WSDM, page 1127-1130. ACM, (2021)MetaLearning with Graph Neural Networks: Methods and Applications., , , and . SIGKDD Explor., 23 (2): 13-22 (2021)FS-EHS: Harmony Search Based Feature Selection Algorithm for Steganalysis Using ELM., , , and . IBICA, volume 424 of Advances in Intelligent Systems and Computing, page 393-402. Springer, (2015)A survey of algorithms for dense subgraph discovery, , , and . Managing and Mining Graph Data, Springer, (2010)Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks., , , , , , , , , and . ACM Comput. Surv., 56 (5): 126:1-126:42 (May 2024)Editorial Deep Learning for Anomaly Detection., , , and . IEEE Trans. Neural Networks Learn. Syst., 33 (6): 2282-2286 (2022)Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection., , , and . IEEE Intell. Syst., 38 (2): 32-35 (March 2023)