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Toward Understanding and Evaluating Structural Node Embeddings., , , and . ACM Trans. Knowl. Discov. Data, 16 (3): 58:1-58:32 (2022)Distribution of Node Embeddings as Multiresolution Features for Graphs., , and . ICDM, page 289-298. IEEE, (2019)Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective., , and . CoRR, (2023)Bridging Network Embedding and Graph Summarization., , , , , and . CoRR, (2018)Events and Controversies: Influences of a Shocking News Event on Information Seeking., , and . WWW, page 614-624. ACM, (2015)Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices., , , , and . WWW, page 1538-1549. ACM, (2022)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)The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)., , , , , , , , and . KDD, page 5870-5871. ACM, (2023)Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks., , , , and . KDD, page 1223-1234. ACM, (2023)