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Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges.
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Computing top-k Closeness Centrality Faster in Unweighted Graphs
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Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 1025–1035
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Graph evolution: Densification and shrinking diameters
Jure Leskovec, Jon M. Kleinberg, and Christos Faloutsos. 2006 · 2006
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Centrality and Connectivity in Public Transport Networks and their Significance for Transport Sustainability in Cities
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The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009 · 2008
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017 · 2017
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Measuring the Complexity of Urban Form and Design
Geoff Boeing. 2018 · 2018
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From structure to activity: Using centrality measures to predict neuronal activity
Jack McKay Fletcher and Thomas Wennekers. 2018 · 2018
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GraphR: Accelerating Graph Processing Using ReRAM. In 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) . IEEE Computer Society, Los Alamitos, CA, USA, 531–543
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STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
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Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 539–548
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Meta-GNN: On Few-Shot Node Classification in Graph Meta-Learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China) (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 2357–2360
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
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Evaluating clustering results
Vijini Mallawaarachchi. 2020 · 2020
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Robust Graph Representation Learning via Neural Sparsification. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Hal Daumé III and Aarti Singh (Eds.), Vol. 119. PMLR, 11458–11468
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EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . 1061–1067
Andac Demir, Toshiaki Koike-Akino, Ye Wang, Masaki Haruna, and Deniz Erdogmus. 2021 · 2021
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Mint: An Accelerator For Mining Temporal Motifs. In 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) . IEEE Computer Society, Los Alamitos, CA, USA, 1270–1287
N. Talati, H. Ye, S. Vedula, K. Chen, Y. Chen, D. Liu, Y. Yuan, D. Blaauw, A. Bronstein, T. Mudge, and R. Dreslinski. 2022 · 2022
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A Generic Graph Sparsification Framework using Deep Reinforcement Learning. In 2022 IEEE International Conference on Data Mining (ICDM) . IEEE Computer Society, Los Alamitos, CA, USA, 1221–1226
R. Wickman, X. Zhang, and W. Li. 2022 · 2022
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Spanning Tree
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Clustering coefficient
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