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Found 7 papers, 3 papers with code
Maximal Independent Sets for Pooling in Graph Neural Networks
Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling.
Maximal Independent Vertex Set applied to Graph Pooling
Consequently, our method does not discard any vertex information nor artificially increase the density of the graph.
A new Sinkhorn algorithm with Deletion and Insertion operations
Conversely, the remaining elements of V2 correspond to the image of the epsilon pseudo element of V1.
Breaking the Limits of Message Passing Graph Neural Networks
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemented and still raise a lot of interest even though their theoretical expressive power is limited to the first order Weisfeiler-Lehman test (1-WL).
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective
Since the graph isomorphism problem is NP-intermediate, and Weisfeiler-Lehman (WL) test can give sufficient but not enough evidence in polynomial time, the theoretical power of GNNs is usually evaluated by the equivalence of WL-test order, followed by an empirical analysis of the models on some reference inductive and transductive datasets.
Generalized Median Graph via Iterative Alternate Minimizations
Computing a graph prototype may constitute a core element for clustering or classification tasks.
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
In this work, we propose a thorough investigation and comparison of graph kernels based on different linear patterns, namely walks and paths.
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Graph Classification
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