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On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications.

, , , , , and . ACM Trans. Knowl. Discov. Data, 14 (5): 63:1-63:37 (2020)

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Anomaly detection in dynamic networks: a survey, , , , , and . Wiley Interdisciplinary Reviews: Computational Statistics, 7 (3): 223--247 (2015)SURREAL: SUbgraph Robust REpresentAtion Learning., , , and . CoRR, (2018)Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit., , , , , , and . CoRR, (2017)NetSimile: A Scalable Approach to Size-Independent Network Similarity, , , and . CoRR, (2012)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)SpecGreedy: Unified Dense Subgraph Detection., , , , and . ECML/PKDD (1), volume 12457 of Lecture Notes in Computer Science, page 181-197. Springer, (2020)