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DeltaCon: Principled Massive-Graph Similarity Function with Attribution.

, , , , and . ACM Trans. Knowl. Discov. Data, 10 (3): 28:1-28:43 (2016)

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TimeCrunch: Interpretable Dynamic Graph Summarization., , , , and . KDD, page 1055-1064. ACM, (2015)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)NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases., , and . EMNLP (1), page 5633-5646. Association for Computational Linguistics, (2021)Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs., , , , , and . NeurIPS, (2020)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)Bridging Network Embedding and Graph Summarization., , , , , and . CoRR, (2018)Distribution of Node Embeddings as Multiresolution Features for Graphs., , and . ICDM, page 289-298. IEEE, (2019)Events and Controversies: Influences of a Shocking News Event on Information Seeking., , and . WWW, page 614-624. ACM, (2015)