Graph Classification
PulseAugur coverage of Graph Classification — every cluster mentioning Graph Classification across labs, papers, and developer communities, ranked by signal.
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New graph dictionary learning framework uses optimal transport for improved classification
Researchers have developed a new graph dictionary learning (GDL) framework that represents graphs as zero-mean Gaussian distributions derived from their filtered Laplacian. This framework approximates observed graphs us…
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New method enhances explainability of Temporal Graph Networks
Researchers have developed a new method to explain the predictions of Temporal Graph Networks (TGNs) by focusing on their memory modules. This approach utilizes a topology attribution tree to assess the influence of nei…
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New framework uses Network Usable Information for graph classification
Researchers have introduced NetinfoGC, a novel framework for graph classification that leverages the Network Usable Information (NUI) paradigm. This approach moves beyond traditional end-to-end neural network training b…
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New logic-based graph learning method rivals GNNs in speed and performance
Researchers have developed new variants of the Weisfeiler-Leman algorithm for graph classification, which involve modifying the underlying logical framework. These variants allow graph data to be tabularized, enabling t…