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A Provable Framework of Learning Graph Embeddings via Summarization., , , , and . AAAI, page 4946-4953. AAAI Press, (2023)Neural Execution Engines: Learning to Execute Subroutines., , , , and . NeurIPS, (2020)CoDEx: A Comprehensive Knowledge Graph Completion Benchmark., and . EMNLP (1), page 8328-8350. Association for Computational Linguistics, (2020)Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data., , , , , , and . CoRR, (2020)Net-Ray: Visualizing and Mining Billion-Scale Graphs., , , and . PAKDD (1), volume 8443 of Lecture Notes in Computer Science, page 348-361. Springer, (2014)Graph based anomaly detection and description: a survey., , and . Data Min. Knowl. Discov., 29 (3): 626-688 (2015)Learning node embeddings via summary graphs: a brief theoretical analysis., , , , and . CoRR, (2022)The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability.. Proc. VLDB Endow., 14 (13): 3416 (2021)From Static to Dynamic Node Embeddings., , , and . CoRR, (2020)Analyzing Data-Centric Properties for Contrastive Learning on Graphs., , , , and . CoRR, (2022)