Zobrazeno 1 - 10
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pro vyhledávání: '"Levie P"'
The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities -- making meaningful predictions
Externí odkaz:
http://arxiv.org/abs/2412.07106
We analyze the universality and generalization of graph neural networks (GNNs) on attributed graphs, i.e., with node attributes. To this end, we propose pseudometrics over the space of all attributed graphs that describe the fine-grained expressivity
Externí odkaz:
http://arxiv.org/abs/2411.05464
Autor:
Zilberg, Daniel, Levie, Ron
We propose PieClam (Prior Inclusive Exclusive Cluster Affiliation Model): a probabilistic graph model for representing any graph as overlapping generalized communities. Our method can be interpreted as a graph autoencoder: nodes are embedded into a c
Externí odkaz:
http://arxiv.org/abs/2409.11618
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving generalization. In this paper, we focus on an extension of shift equiva
Externí odkaz:
http://arxiv.org/abs/2406.01249
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates a memory complexity of the ord
Externí odkaz:
http://arxiv.org/abs/2405.20724
We study the generalization capabilities of Message Passing Neural Networks (MPNNs), a prevalent class of Graph Neural Networks (GNN). We derive generalization bounds specifically for MPNNs with normalized sum aggregation and mean aggregation. Our an
Externí odkaz:
http://arxiv.org/abs/2404.03473
Autor:
Morris, Christopher, Frasca, Fabrizio, Dym, Nadav, Maron, Haggai, Ceylan, İsmail İlkan, Levie, Ron, Lim, Derek, Bronstein, Michael, Grohe, Martin, Jegelka, Stefanie
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences. Despite their pra
Externí odkaz:
http://arxiv.org/abs/2402.02287
To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview paper, we briefly describe the pa
Externí odkaz:
http://arxiv.org/abs/2310.07658
Autor:
John Whitaker, Ella Togun, Levie Gondwe, Donaria Zgambo, Abena S. Amoah, Albert Dube, Rory Rickard, Andrew JM Leather, Justine Davies
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-17 (2024)
Abstract Introduction The global burden of injury is huge, falling disproportionately on poorer populations. The benefits of qualitative research in injury care are recognised and its application is growing. We used a novel application of focus group
Externí odkaz:
https://doaj.org/article/84b9633b63224e6db969bb0958be2077
Graph neural networks (GNNs) are commonly described as being permutation equivariant with respect to node relabeling in the graph. This symmetry of GNNs is often compared to the translation equivariance of Euclidean convolution neural networks (CNNs)
Externí odkaz:
http://arxiv.org/abs/2308.10436