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Fast and memory-efficient algorithms for high-order Tucker decomposition., , , , , and . Knowl. Inf. Syst., 62 (7): 2765-2794 (2020)Two-Stage Training of Graph Neural Networks for Graph Classification., , and . Neural Process. Lett., 55 (3): 2799-2823 (June 2023)Hypercore Decomposition for Non-Fragile Hyperedges: Concepts, Algorithms, Observations, and Applications., , and . CoRR, (2023)SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning., , and . CoRR, (2024)Fast, Accurate, and Flexible Algorithms for Dense Subtensor Mining., , and . ACM Trans. Knowl. Discov. Data, 12 (3): 28:1-28:30 (2018)Patterns and anomalies in k-cores of real-world graphs with applications., , and . Knowl. Inf. Syst., 54 (3): 677-710 (2018)BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs., , , and . SIGMOD Conference, page 1571-1585. ACM, (2015)S-HOT: Scalable High-Order Tucker Decomposition., , , , and . WSDM, page 761-770. ACM, (2017)I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs., and . AAAI, page 8456-8464. AAAI Press, (2023)Mining of Real-world Hypergraphs: Patterns, Tools, and Generators., , and . CIKM, page 5144-5147. ACM, (2022)