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Found 4 papers, 2 papers with code
Generalizing Downsampling from Regular Data to Graphs
Downsampling produces coarsened, multi-resolution representations of data and it is used, for example, to produce lossy compression and visualization of large images, reduce computational costs, and boost deep neural representation learning.
K-plex Cover Pooling for Graph Neural Networks
We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern.
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions.
Proximity Search For Maximal Subgraph Enumeration
Using known techniques, such as reverse search, the space of all maximal solutions induces an implicit directed graph called "solution graph" or "supergraph", and solutions are enumerated by traversing it; however, nodes in this graph can have exponential out-degree, thus requiring exponential time to be spent on each solution.
Data Structures and Algorithms 05C30, 05C85 F.2.2
