Find all the TopGraphs papers. Links to pdf, code repos and demos are provided.
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measur...
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Introduces the Graph-based Full Event Interpretation (GraFEI), a machine learning model utilizing graph neural networks to reconstruct events at the B...
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We propose a nonparametric approach to link prediction in large-scale dynamic
networks. Our model uses graph-based features of pairs of nodes as well ...
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Submodular functions describe a variety of discrete problems in machine
learning, signal processing, and computer vision. However, minimizing
submodul...
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Presents L-Shapley and C-Shapley, two innovative algorithms for instancewise feature importance scoring tailored to structured data, particularly wher...
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Given a weighted graph with vertices, consider a real-valued regression
problem in a semi-supervised setting, where one observes labeled verti...
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Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP infere...
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Introduces C4 and ClusterWild!, two novel parallel algorithms for correlation clustering (CC) designed to enhance performance on large graphs. The alg...
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Designs methods for decentralized multiple hypothesis testing on graphs that are equipped with provable guarantees on the false discovery rate (FDR). ...
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Presents a novel nonparametric approach to link prediction in dynamic networks, addressing challenges faced by traditional heuristic methods. The prop...
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Provides a comprehensive review of adversarial attacks and defenses in deep neural networks (DNNs) across three data types: images, graphs, and text. ...
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