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CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment.

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TimeCrunch: Interpretable Dynamic Graph Summarization., , , , and . KDD, page 1055-1064. ACM, (2015)Fast anomaly detection despite the duplicates., , , and . WWW (Companion Volume), page 195-196. International World Wide Web Conferences Steering Committee / ACM, (2013)Bridging Network Embedding and Graph Summarization., , , , , and . CoRR, (2018)Toward Understanding and Evaluating Structural Node Embeddings., , , and . ACM Trans. Knowl. Discov. Data, 16 (3): 58:1-58:32 (2022)Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective., , and . CoRR, (2023)Distribution of Node Embeddings as Multiresolution Features for Graphs., , and . ICDM, page 289-298. IEEE, (2019)Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks., , , , and . KDD, page 1223-1234. ACM, (2023)The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)., , , , , , , , and . KDD, page 5870-5871. ACM, (2023)Mining Persistent Activity in Continually Evolving Networks., , and . KDD, page 934-944. ACM, (2020)Coding Varied Behavior Types Using the Crowd., , , , , , , , , and . CSCW Companion, page 114-117. ACM, (2016)